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US History/Constitution Early Years

Early Immigration to the Americas as of 1790
The following table is an approximation of the countries of origin for new arrivals to United States up to 1790. The regions marked * were a part of Great Britain. The Irish in the 1790 census were probably mostly Irish Protestants and the French Huguenots. The total U.S. Catholic population in 1790 was probably less than 5%.

James Webb, among others, has argued that not enough credit is given to early Scots-Irish for the role they played in early American history. These people formed a full 40% of the American Revolutionary army: their culture is now dominant in the American South, Midwest and Appalachian Region.

Failures Under Confederation
The original constitution as defined in the Articles of Confederation was meant to provide a league of sovereign states, rather than a united government. However, the uniting of these states in the Revolution showed flaws in this legislation. From place to place the same goods and services had a wide variation in price, and a wide variation in the way they were paid for. Should they be paid with trade in kind, as with many rural communities; in tobacco, as in Virginia; in gold and silver ores; or in Spanish Dollars or British Pound Sterling? If the last, how to establish that the states were neither Spanish nor British? Hard currency was in short supply following the American Revolution. The Revolution had been paid for in Continental specie: inflation left these bills "not worth a Continental." Several states had also printed paper currency, but it, like the Continental dollar, was heavily depreciated. Article Twelve said that War debts would be paid for by the central government, but Article Eight said that these monies would be raised by State legislatures. Without a strong central authority, how could equality of funding be assured? In fact, none of the debts, individual or national, had yet been paid, since the confederation had not the power of taxation. With all their rich resources, the States were running a trade deficit, buying many of their goods from the British. The confederation was also powerless to defend American navigational rights, evict the British from forts they were holding in violation of the Treaty of Paris, or settle disputes between two states.

The bankrupt confederation had trouble paying the soldiers who fought in the Revolutionary War. This led to the Newburgh Conspiracy, an planned overthrow of the confederation which would have installed General Washington as head of a military junta. Washington, not interested in being a military dictator, quelled the cabal, negotiated pay and pensions for his soldiers, and resigned his commission, returning to civilian life at Mount Vernon.

One event that made it clear that some reform of the Articles was necessary was Shays' Rebellion. Thousands of disgruntled and impoverished farmer-soldiers, led by Daniel Shays, shut down courthouses and led an (ultimately unsuccessful) military uprising against the government of Massachusetts.

The Constitutional Convention
In 1787, a Convention was called in Philadelphia with the declared purpose of revising the Articles of Confederation. However many delegates intended to use this convention for the purpose of drafting a new constitution. All states except for Rhode Island sent delegates, though all delegates did not attend. It was presided over by George Washington. Other leading figures at the convention included James Madison and John Randolph of Virginia, Ben Franklin, Gouverneur Morris and Robert Morris of Pennsylvania and Alexander Hamilton of New York. At the convention, the primary issue was representation of the states. Under the Articles, each state had one vote in Congress. The more populous states wanted representation to be based on population (proportional representation). James Madison of Virginia crafted the Virginia Plan, which guaranteed proportional representation and granted wide powers to the Congress. The small states, on the other hand, supported equal representation through William Paterson's New Jersey Plan. The New Jersey Plan also increased the Congress' power, but it did not go nearly as far as the Virginia Plan. The conflict threatened to end the Convention, but Roger Sherman of Connecticut proposed the "Great Compromise" (or Connecticut Compromise) under which one house of Congress would be based on proportional representation, while the other would be based on equal representation. Eventually, the Compromise was accepted and the Convention saved.

After settling on representation, compromises seemed easy for other issues. The question about the counting of slaves when determining the official population of a state was resolved by the Three-Fifths Compromise, which provided that slaves would count as three-fifths of persons. In another compromise, the Congress was empowered to ban the slave trade, but only after 1808. Similarly, issues relating to the empowerment and election of the President were resolved, leading to the Electoral College method for choosing the Chief Executive of the nation.

The Federalist Papers and Ratification
The Convention required that the Constitution come into effect only after nine states ratify, or approve, it. The fight for ratification was difficult, but the Constitution eventually came into effect in 1788.

During 1788 and 1789, there were 85 essays published in several New York State newspapers, designed to convince New York and Virginia voters to ratify the Constitution. The three people who are generally acknowledged for writing these essays are Alexander Hamilton, James Madison and John Jay. Since Hamilton, Madison, and Jay were considered Federalists, this series of essays became known as "The Federalist Papers. One of the most famous Federalist Papers is Federalist No. 10, which was written by Madison and argues that the checks and balances in the Constitution prevent the government from falling victim to factions.

Anti-Federalists did not support ratification. Many individuals, such as Patrick Henry, George Mason, and Richard Henry Lee, were Anti-Federalists. The Anti-Federalists had several complaints with the constitution. One of their biggest was that the Constitution did not provide for a Bill of Rights protecting the people. They also thought the Constitution gave too much power to the federal government and too little to individual states. A third complaint of the Anti-Federalists was that Senators and the President were not directly elected by the people, and that the House of Representatives was elected every two years instead of annually.

On December 7, 1787, Delaware was the first state to ratify the Constitution. The vote was unanimous, 30-0. Pennsylvania followed on December 12 and New Jersey on ratified on December 18, also in a unanimous vote. By summer 1788, Georgia, Connecticut, Massachusetts, Maryland, South Carolina, New Hampshire, Virginia and New York had ratified the Constitution, and it went into effect. On August 2, 1788, North Carolina refused to ratify the Constitution without amendments, but it relented and ratified it a year later.

A number of states that ratified the Constitution also issued statements requesting changes to the constitution, such as a Bill of Rights. This led to the Bill of Rights being drawn up in the first few years of the federal government.

The Bill of Rights
George Washington was inaugurated as the first United States president on April 30, 1789. However, North Carolina, Rhode Island and Vermont had not ratified the Constitution. Although governance was assured, there were no protections of people's religion, speech, or civil liberties. On September 26, 1789, Congress sent a list of twelve amendments to the states for ratification. Ten of the amendments would become the Bill of Rights. North Carolina ratified the Constitution in November of 1789, followed by Rhode Island in May 1790. Vermont became the last state to ratify the Constitution on January 10, 1791, becoming the 14th state in the Union.

The Bill of Rights was enacted on December 15, 1791. Here is a summary of the ten amendments ratified on that day:

 * 1) Establishes freedom of religion, speech, the press, assembly, petition.
 * 2) Establishes the right to keep and bear arms as part of a well-regulated militia.
 * 3) Bans the forced quartering of soldiers.
 * 4) Interdiction of unreasonable searches and seizures; a search warrant is required to search persons or property.
 * 5) Details the concepts of indictments, due process, self-incrimination, double jeopardy, rules for eminent domain.
 * 6) Establishes rights to a fair and speedy public trial, to a notice of accusations, to confront the accuser, to subpoenas, and to counsel.
 * 7) Provides for the right to trial by jury in civil cases
 * 8) Bans cruel and unusual punishment, and excessive fines or bail
 * 9) Lists unremunerated rights
 * 10) Limits the powers of the federal government to only those specifically granted by the constitution.

Washington Administration
After George Washington had become the successful Commander in Chief of the Continental Army in the American Revolutionary War, he became the first President of the United States, holding office from 1789 to 1797.

In 1788, the Electors unanimously chose Washington as president. A short time after he had helped bring the government together, rivalries arose between his closest advisors, particularly between Secretary of State Thomas Jefferson and Secretary of the Treasury Alexander Hamilton. Out of these developments evolved two new political parties: The Federalists, who shared the same name as the earlier pro-ratification party, and the Democratic-Republican Party, also known as the Jeffersonian party, or as the Anti-Federalists.

Washington's two-term administration set many policies and traditions that survive today. He was again unanimously elected in 1792. But after his second term expired, Washington again voluntarily relinquished power, thereby establishing an important precedent that was to serve as an example for the United States and also for other future republics. Washington also abjured titles. He didn't want to be called "Your Excellency" or "Your Majesty." He insisted on being called "Mr. President," and referred to as "The President of the United States". Because of his central role in the founding of the United States, Washington is often called the "Father of his Country". Scholars rank him with Abraham Lincoln among the greatest of United States presidents.

Hamilton's Financial Plan
One of the main conflicts between the Federalists and the Anti-Federalists was over how to pay off Revolutionary War Debts. In 1790, Alexander Hamilton wrote his First Report on Public Credit to achieve this end. The report advocated that the federal government assume, or take over, state debts and turn them into one national debt. However, Jefferson and the anti-Federalists criticized this plan. In the Compromise of 1790, the federal government was allowed to assume state debts in exchange for the national capital being relocated to the District of Columbia.

In the Second Report on Public Credit, Hamilton argued that a National Bank was necessary to expand the flow of legal tender and encourage investment in the United States. Jefferson claimed that the creation of the National Bank violated the 10th Amendment of the Constitution, and therefore was unconstitutional. Hamilton responded that the bank was constitutional because the constitution gave "implied powers" to the Federal Government to do what was "necessary and proper" to fulfill its duties. Legislation creating the bank was passed in February 1791, and gave the bank a charter of twenty years.

Jefferson did not agree with Hamilton's idea of a national bank. Jefferson's faction envisioned an America more similar to ancient Athens or pre-Imperial Rome, with independent farming households following their own interests and nurturing liberty. Jefferson believed America should teach people to be self-sufficient farmers, and he wanted the federal government to stop interfering in state matters. (However, some evidence suggests Jefferson did support Hamilton's plan for paying off state debts. He wanted leverage to pressure Hamilton's agreement to locate the government's permanent capital in the South, on the Potomac River in what would become Washington, D.C.)

Both President Washington and Congress agreed to Hamilton's Bank. Jefferson's plan was overruled, and he eventually resigned as Secretary of State.

Whiskey Rebellion (1794)
Washington was involved in one controversy during his presidency, the Whiskey Rebellion of 1794. The new Republic needed funds. This motivated Alexander Hamilton to press Congress to pass an excise tax on the sale of whiskey. Rural Pennsylvania farmers, who had never known a centralized American authority, were horrified by a call on what they considered their own profit and refused to pay the tax. A mob of 500 men attacked a tax collector's house. In response, Washington and Hamilton led an army of 15,000 men to quell the rebellion, an army larger than the force Washington had commanded in the American Revolution. When the army showed up, the rebels dispersed. The whiskey tax was eventually repealed by the Democratic Republicans in 1801.

The French Revolution
The French Revolution broke out in 1789, a few months after the American Constitution had gone into effect. At first, as France overthrew the monarchy and declared itself a republic, many Americans supported the revolution. They believed their own revolt against England had spurred France to republicanism. But as the Reign of Terror began, and thousands of French aristocrats went to the guillotine, many Americans were shocked at the revolution's excesses. By the mid-1790s, as France went to war against neighboring monarchies, the revolution polarized American public opinion. Federalists viewed England, France's traditional enemy, as the bastion of stable government against a growing tide of French anarchy. Members of the emerging Democratic Republican Party, on the other hand, who took their party's name in part from the French Republic, believed the Terror to be a temporary excess and continued to view England as the true enemy of liberty.

President Washington's policy was neutrality. He knew that England, France, or even Spain, would be happy to eat up American resources and territory. The United States in the 1790s was still new and frail. He hoped that America could stay out of European conflicts until it was strong enough to withstand any serious foreign threat. Yet both England and France found opportunities to each use American resources against the other.

Hamilton and Jefferson clashed here as well. The former argued that the mutual defense treaty that the United States had signed with France in 1778 was no longer binding, as the French regime that had made that treaty no longer existed. The latter disagreed. But Washington sided with Hamilton, issuing a formal Proclamation of Neutrality in 1793. Washington repeated his belief in neutrality and argued against factionalism in his Farewell Address of 1796.

That same year, Citizen Edmund Charles Genêt arrived as the French minister to the United States. He soon began issuing commissions to captains of American ships who were willing to serve as privateers for France. This blatant disregard of American neutrality angered Washington, who demanded and got Genêt's recall.

English and Spanish Negotiations
The Royal Navy, meanwhile, began pressing sailors into service, including sailors on American merchant ships. Many English sailors had been lured into the American merchant service by high wages and comparatively good standards of living, and England needed these sailors to man its own fleet, on which England's national security depended. This violation of the American flag, however, infuriated Americans, as did the fact that England had not yet withdrawn its soldiers from posts in the Northwest Territory, as required by the Treaty of Paris of 1783.

In response, President Washington sent Supreme Court Chief Justice John Jay to negotiate a treaty with England. But Jay had little leverage with which to negotiate: the final treaty did require immediate English evacuation of the frontier forts, but it said nothing about the matter of impressments. The Jay Treaty provoked an outcry among American citizens, and although the Senate ratified it narrowly, the debate it sparked was the final blow which solidified the Federalist and Republican factions into full-scale political parties, Federalists acquiescing in the treaty, and Republicans viewing it as a sell-out to England (and against France).

Spain, meanwhile, viewed the Jay Treaty negotiations with alarm, fearing that America and England might be moving towards an alliance. Without being certain of the treaty provisions, Spain decided to mollify the United States and give ground in the southwest before a future Anglo-American alliance could take New Orleans and Louisiana. Spain thus agreed to abandon all territorial claims north of Florida and east of the Mississippi, with the exception of New Orleans, and to grant the United States both the right to navigate the Mississippi and the right of commercial deposit in New Orleans. This would give westerners greater security and allow them to trade with the outside world. This Treaty of San Lorenzo, also called Pinckney's Treaty after American diplomat Charles Pinckney, was signed in 1795 and ratified the following year. Unlike Jay's treaty, it was quite popular.

If Jay's Treaty alarmed Spain, it angered France, which saw it as a violation of the Franco-American mutual defense treaty of 1778. By 1797, French privateers began attacking American merchant shipping in the Caribbean.

Election of 1796
The first election where the presidency was not a foregone conclusion produced four candidates: Washington's Federalist Vice President John Adams, Democratic-Republican Thomas Jefferson, the Federalist Thomas Pinckney, and the Democratic-Republican Aaron Burr. (Burr was in fact hoping to become Vice President if the other Democratic-Republican were to win.) There were no Vice Presidential candidates, for under the original draft of the Constitution, the Vice President would be the candidate with the second-largest number of votes.

This was also the election testing the Constitution's Electoral College. Voters in each state did not directly elect candidates. Instead, their choices directed the two votes cast by the state's Electors.

In the election of 1796, John Adams won the required majority, but Thomas Jefferson came in second place. The President and the Vice President were in two different parties. The Constitutional role of the Vice President was defined in relation to the President: there was little else for him to do. Jefferson was isolated from an administration which supported strong government and a central Bank, and his contrary views were not consulted in the Adams administration.

The XYZ Affair
Newly-elected President John Adams sent a delegation to Paris, resolving to negotiate a settlement with France. However, the delegates found it impossible even to secure an appointment with Talleyrand, the French foreign minister. The delegates were approached by three minor functionaries who insisted that the Americans pay a bribe to inaugurate negotiations, warning them of "the power and violence of France" if they refused. The delegates refused. ("The answer is no; no; not a sixpence," one of them retorted. This was popularly rendered as "Millions for defense, not a penny for tribute.") When Adams made the correspondence public, after replacing the names of the French functionaries with X, Y, and Z, American sentiment swung strongly against France. Under the control of the Federalists, Congress initiated a military buildup, fielding several excellent warships and calling Washington out of retirement to head the army. (Washington agreed only on condition that he not command until the army actually took the field. The army was never marshaled.)

The result was a Quasi-war, or an undeclared naval war with France. It consisted of combat between individual ships, mostly in the Caribbean, from 1798 to 1800. Eventually the United States and France agreed to end hostilities and to end the mutual defense treaty of 1778. Adams considered this one of his finest achievements.

Alien and Sedition Acts
Under Adams, the Federalist-dominated congress pushed passage of a series of laws under the cover of overcoming dangerous "aliens." In fact, the four acts were used to silence domestic political opponents.

 * 1) The Alien Act authorized the president to deport an alien deemed "dangerous."
 * 2) The Alien Enemies Act authorized the president to deport or imprison any alien from a country that the United States was fighting a declared war with.
 * 3) The Sedition Act made it a crime to criticize government officials and publish "false, scandalous, and malicious writing" against the government or its agents.
 * 4) The Naturalization Act changed the residency requirements for aliens to become citizens from 5 to 14 years.

Although it was openly deemed to be a security act, it provided powerful tools to the ruling Federalist party to quiet opposition from the growing Democratic-Republican Party. By extending the time required to become a citizen, they decreased the number of new voters that might choose to support the minority party. However, these acts were rarely enacted against political opponents due to the possibility of conflict such actions could create.

Education
In well-off families both boys and girls went to a form of infant school called a petty school. However only boys went to what you call grammar school. Upper class girls and sometimes boys were taught by tutors. Middle class girls might be taught by their mothers. Moreover during the 17th century boarding schools for girls were founded in many towns. In the boarding schools girls were taught subjects like writing, music and needlework. In the grammar schools conditions were hard. Boys started work at 6 or 7 in the morning and worked to 5 or 5.30 pm, with breaks for meals. Corporal punishment was usual. Normally the teacher hit naughty boys on the bare butt with birch twigs. Other boys in the class would hold the naughty boy down.

In the 17th and early 18th centuries women were not encouraged to get an education. Some people believed that if women were well educated it would ruin their marriage prospects and be harmful to their mind. Protestants believed that women as well as men should be able to read the bible. Only the daughters of the wealthy or nobility could get an education. By the mid 17th century young women were allowed to go to school with their brothers. Sometimes if you had the money you would be placed within a household of a friend and within the household and you would be taught various things. Some of the things you would learn would be to read and write, run a household, and practice surgery.

After the signing of the Declaration of Independence, several states had their own constitutions and there were sections in them that had information pertaining to education. But Thomas Jefferson had the thought that education should be left up to the government. He believed that education should not have a religious bias in it and believed that it should be free to all people not matter what their social status was. It was still very hard to make the concept of public schools easy for people to accept because of the vast number of people who were immigrating, the many different political views, and the different levels of economic difficulties.

Technology
In the 1790s certain New England weavers began building large, automated looms, driven by water power. To house them they created the first American factories. Working the looms required less skill and more speed than household laborers could provide. The looms needed people brought to them; and they also required laborers who did not know the origin of the word sabotage. These factories sought out young women.

The factory owners said they wanted to hire these women just for a few years, with the ideal being that they could raise a dowry for their wedding. They were carefully supervised, with their time laid out for them. Some mill owners created evening classes to teach these women how to write and how to organize a household.

The factories provided a cheaper source of cotton cloth, sent out on ships and on roads improved by a stronger government. For the first time some people could afford more than two outfits, work and Sunday best. They also provided an outlet for cotton from the slave states in the South. Cotton was at that time one among many crops. Many slaves had to work to separate cotton from the seeds of the cotton plant, and to ship it to cloth-hungry New England. This was made simpler by Eli Whitney's invention of the cotton gin in 1793. Cotton became a profitable crop, and many Southern farms now made it their only crop. Growing and picking cotton was long, difficult labor, and the Southern plantation made it the work for slaves. Northern factories became part of the economy of slavery.

This renewed reliance on slavery was going against the trend in other parts of the country. Vermont had prohibited it in its state constitution in 1777. Pennsylvania passed laws for the gradual abolition of the condition in 1780, and New York State in 1799. Education, resources, and economic development created the beginnings of industrialization in many Northern states and plantations and slavery and less development in states in the Deep South.

Review Questions
1. Identify, and explain the significance of the following people:
 * (a) James Madison
 * (b) William Paterson
 * (c) Alexander Hamilton
 * (d) Patrick Henry
 * (e) Thomas Jefferson
 * (f) George Washington
 * (g) John Adams
 * (h) Edmund Genet
 * (i) Charles Pinckney

2. What was accomplished during the Constitutional Convention in terms of states' representations in the national government?

3. How did Hamilton take measures to ensure the ratification of the Constitution?

4. Name two problems or complaints the Anti-Federalists had with the Constitution.

5. What precedents did George Washington set in his two terms in office?

6. On what issues did Jefferson and Hamilton differ? How did this affect policies during the Washington administration?

7. What was the Whiskey Rebellion? Why was it significant?

8. What did Jay's Treaty and Pinckney's Treaty accomplish?

9. How did the United States respond to the French Revolution?

10. What was the XYZ Affair? What was its result?

11. What was the original purpose of the Alien, Sedition, and Naturalization Acts? How did their purpose change?

title: US History/Constitution Early Years


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User:Juandelenzina

es:Usuario:Juandelenzina

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

title: User:Juandelenzina


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Category:Serbian recipes

Recipes from Serbia.

title: Category:Serbian recipes


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Talk:Learning Theories

Cut from beginning of document
I cut the following from the beginning of the page. It needs some work to incorporate into the body of material.

/Chapter_Overview of Learning Theories Learning_Theories/Chapter_Behaviorist Theories

--152.163.101.6 00:56, 4 April 2006 (UTC)=Cognitive Theories= Cognitive psychology is the basis for organizational learning. According to Hedberg and Wolff(2001),the models were derived from the "stimulus-response mechanism" in which people learn from their interactions with the outside and develop appropriate behaviors based on the feedback which is received. --152.163.101.6 00:56, 4 April 2006 (UTC)

 * @ Ecology/Energy in ecosystems 49.204.181.35 (discuss) 15:15, 12 June 2021 (UTC)

title: Talk:Learning Theories


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US History/States Rights

The Position
One of the main causes for the Civil War was the matter of states' rights. There was always the possibility of the federal and state governments disagreeing on some issue of law. However, there was clear procedure in the constitution for settling this disagreement. The states rights position was that the states had the powers of nullification and secession to check the excesses of the federal government. If nullification was accepted, a state could declare a federal law invalid in its territory. If secession was accepted, a state could leave the union and become an independent country.

The Constitution and States Rights
The US constitution did not take a firm position either way on states rights. In fact it contained provisions that could be interpreted either way.

Article VI, clause 2 states: This Constitution, and the Laws of the United States which shall be made in Pursuance thereof; and all Treaties made, or which shall be made, under the authority of the United States, shall be the supreme Law of the land; and the Judges in every State shall be bound thereby, any Thing in the Constitution or Laws of any State to the Contrary notwithstanding.

The Tenth Amendment states:

The powers not delegated to the United States by the Constitution, nor prohibited by it to the States, are reserved to the States respectively, or to the people.

Because of the 10th amendment combined with no prohibition of secession many held that the states had the power to leave the union at will. Any law to the contrary would violate the constitution. However many believed that the states had no such right because it was not expressly stated and would necessarily void laws passed under the constitution in the seceding states.

The Kentucky and Virginia Resolutions
The Alien and Sedition Acts passed in 1798 generated a lot of opposition. They were correctly seen as a tool to suppress the political opponents of the ruling Federalist Party. Because of this Thomas Jefferson and James Madison drafted resolutions against them. These resolutions were passed by the legislatures of Kentucky and Virginia. They argued that the laws were unconstitutional and therefore were nullified. Other states responded that this was inappropriate and state legislatures could not determine the constitutionality of the laws. The issue of the Alien and Sedition Acts was resolved when Jefferson was elected president 1800. When he took office the laws expired and he pardoned those who had been convicted. However the issue of States Rights was not resolved.

Nullification Crisis
In 1828 Congress raised tariffs on imported goods. This made imported goods more expensive than American manufactured goods. Northern factories benefited from this because imported goods were no longer cheaper than what they produced. In addition, much of the money raised was spent on internal improvements in the north. The southern states paid the majority of the tariff because they sold cotton abroad and bought manufactured goods either from the north or other countries. They viewed the tariff as an unfair law made to benefit the north at their expense. South Carolina passed a Nullification Ordinance declaring that the tariffs were null and void within Carolina. President Andrew Jackson threatened to send in government troops and ships to enforce the tariff. In 1832 he signed a law lowering the tariffs. Because the threat of using troops was made but the federal government backed down without carrying it out, the issue of nullification remained unresolved.

Other Issues
States rights were also invoked for slavery, against the fugitive slave act, and against the War of 1812. However at no point were the questions of states rights answered before the civil war.

title: US History/States Rights


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User talk:Gesiwuj~enwikibooks

Image:Usb-ps2-comparison.png
Why do you think image from a book is public domain? --Derbeth talk 21:13, 12 April 2006 (UTC)

Image:Vga-dvi-comparison.png
What is the source of this image? --Derbeth talk 08:28, 19 April 2006 (UTC)

Your account will be renamed
Hello,

The developer team at Wikimedia is making some changes to how accounts work, as part of our on-going efforts to provide new and better tools for our users like cross-wiki notifications. These changes will mean you have the same account name everywhere. This will let us give you new features that will help you edit and discuss better, and allow more flexible user permissions for tools. One of the side-effects of this is that user accounts will now have to be unique across all 900 Wikimedia wikis. See the announcement for more information.

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title: User talk:Gesiwuj~enwikibooks


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Talk:Computer Science/Data Structures/Asymptotic Notation content

Talldean 21:14, 12 April 2006 (UTC): Saw this needed some work, and am working on it. My apologies for any errors (factual or typographical), but need to head to dinner, and there's no "draft" save that I know about. Feel free to edit my edits, or let me know if I'm off track. Basically, my main goal is taking what's there, and making it a more useful reference for people who haven't seen the concept before.


text:
User talk:Darrenferguson

Welcome
Hello and welcome to Wikibooks!

Here are some tips to help you get started:
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User:Talldean

Something should go here.

-Dean Jackson, 2006-4-12

title: User:Talldean


text:
User talk:Sundström

Welcome
Hello and welcome to Wikibooks!

Here are some tips to help you get started:
 * You can sign your name on Talk and vote pages using four tildes, like this: ~~~~.
 * Remember to conduct any editing experiments in the sandbox.
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 * Take a look at Naming policy
 * See list of templates which can save your time
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 * Explore, be bold in editing pages, and have fun!

You will find more resources in Community Portal. If you want to ask a question, visit the Study help desk, the Staff lounge, IRC channel or ask me personally on my talk page.

Good luck! --Derbeth talk 21:17, 12 April 2006 (UTC)

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Talk:False Friends of the Slavist/Map divan

Untitled
In Bulgaria divan means sofa.

 * In Bunjevac language, divan means language. This meaning is also present in Serbo-Croatian, but as an archaic word. --George D. Bozovic talk 18:00, 13 September 2006 (UTC)

In Ukrainian диван means sofa. In the sence carpet exists word килим. Szugar (talk) 19:17, 17 August 2008 (UTC)

Serbian "beautiful" meaning
I'm not fully sure, but it seems that Serbian divan "beautiful" have nothing common Turkish divan, it's Slavic root divьnъ, Croatian has this word too. -- Upliner (discuss • contribs) 11:21, 28 October 2015 (UTC)


text: User talk:Mmirzaee

title: User talk:Mmirzaee


text:
User:Lbartels

Hi,

this is my page.

I am a Cognitive Sciene Bachelor Student and at Wikibooks contributing to the CPNP - project. Cognitive Psychology and Cognitive Neuroscience.

title: User:Lbartels


text:
Cognitive Psychology and Cognitive Neuroscience/Knowledge Representation and Hemispheric Specialisation

Introduction
Most human cognitive abilities rely on or interact with what we call knowledge. How do people navigate through the world? How do they solve problems, how do they comprehend their surroundings and on which basis do people make decisions and draw inferences? For all these questions, knowledge, the mental representation of the world is part of the answer.

What is knowledge? According to Merriam-Websters online dictionary, knowledge is “the range of one’s information and understanding” and “the circumstance or condition of apprehending truth or fact through reasoning”. Thus, knowledge is a structured collection of information, that can be acquired through learning, perception or reasoning.

This chapter deals with the structures both in human brains and in computational models that represent knowledge about the world. First, the idea of concepts and categories as a model for storing and sorting information is introduced, then the concept of semantic networks and, closely related to these ideas, an attempt to explain the way humans store and handle information is made. Apart from the biological aspect, we are also going to talk about knowledge representation in artificial systems which can be helpful tools to store and access knowledge and to draw quick inferences.

After looking at how knowledge is stored and made available in the human brain and in artificial systems, we will take a closer look at the human brain with regard to hemispheric specialisation. This topic is not only connected to knowledge representation, since the two hemispheres differ in which type of knowledge is stored in each of them, but also to many other chapters of this book. Where, for example, is memory located, and which parts of the brain are relevant for emotions and motivation? In this chapter we focus on the general differences between the right and the left hemisphere. We consider the question whether they differ in what and how they process information and give an overview about experiments that contributed to the scientific progress in this field.

Concepts and Categories
For many cognitive functions, concepts are essential. Concepts are mental representations, including memory, reasoning and using/understanding language. One function of concepts is the categorisation of knowledge which has been studied intensely. In the course of this chapter, we will focus on this function of concepts.

Imagine you wake up every single morning and start wondering about all the things you have never seen before. Think about how you would feel if an unknown car parked in front of your house. You have seen thousands of cars but since you have never seen this specific car in this particular position, you would not be able to provide yourself with any explanation. Since we are able to find an explanation, the questions we need to ask ourselves are: How are we able to abstract from prior knowledge and why do we not start all over again if we are confronted with a slightly new situation? The answer is easy: We categorise knowledge. Categorisation is the process by which things are placed into groups called categories. Categories are so called “pointers of knowledge”. You can imagine a category as a box, in which similar objects are grouped and which is labeled with common properties and other general information about the category. Our brain does not only memorise specific examples of members of a category, but also stores general information that all members have in common and which therefore defines the category. Coming back to the car-example, this means that our brain does not only store how your car, your neighbors’ and your friends’ car look like, but it also provides us with the general information that most cars have four wheels, need to be fueled and so on. Because categorisation immediately allows us to get a general picture of a scene by allowing us to recognise new objects as members of a category, it saves us much time and energy that we otherwise would have to spend in investigating new objects. It helps us to focus on the important details in our environment, and enables us to draw the correct inferences. To make this obvious, imagine yourself standing at the side of a road, wanting to traverse it. A car approaches from the left. Now, the only thing you need to know about this car is the general information provided by the category, that it will run you over if you don't wait until it has passed. You don't need to care about the car's color, number of doors and so on. If you were not able to immediately assign the car to the category "car", and infer the necessity to step back, you would get hit because you would still be busy with examining the details of that specific and unknown car. Therefore categorisation has proved itself as being very helpful for surviving during evolution and allows us to quickly and efficiently navigate through our environment.

Definitional Approach
Take a look at the following picture! You will see four different kinds of cars. They differ in shape, color and other features, nonetheless you are probably sure that they are all cars.

What makes us so convinced about the identity of these objects? Maybe we can try to find a definition which describes all these cars. Have all of them four wheels? No, There are some which have only three. Do all cars drive with petrol? No, That's not true for all cars either. Apparently we will fail to come up with a definition. The reason for this failure is that we have to generalise to make a definition. That would work perhaps for geometrical objects, but obviously not for natural things. They do not share completely identical features in one category for that it is problematic to find an appropriate definition. There are however similarities between members of one category, so what about this familiarity? The famous philosopher and linguist Ludwig Wittgenstein asked himself this question and claimed to have found a solution. He developed the idea of family resemblance. That means that members of a category resemble each other in several ways. For example cars differ in shape, color and many other properties but every car resembles somehow other cars. The following two approaches determines categories by similarity.

Prototype Approach
The prototype approach was proposed by Rosch in 1973. A prototype is an average case of all members in a particular category, but it is not an actual, really existent member of the category. Even extreme various features of members within one category can be explained by this approach. Different degrees of prototypicality represent differences among category- members. Members which resemble the prototype very strongly are high-prototypical. Members which differ in a lot of ways from the prototype are therefore low-prototypical. There seem to be connections to the idea of family resemblance and indeed some experiments showed that high prototypicality and high family resemblance are strongly connected. The typicality effect describes the fact that high-prototypical members are faster recognised as a member of a category. For example participants had to decide whether statements like “A penguin is a bird.” or “A sparrow is bird.” are true. Their decisions were much faster concerning the “sparrow” as a high-prototypical member of the category “bird” than for an atypical member as “penguin”. Participants also tend to prefer prototypical members of a category when asked to list objects of a category. Concerning the birds-example, they rather list “sparrow” than “penguin”, which is a quite intuitive result. In addition high-prototypical objects are strongly affected by priming.

Exemplar Approach
The typicality effect can also be explained by a third approach which is concerned with exemplars. Similar to a prototype, an exemplar is a very typical member of the category. The difference between exemplars and prototypes is that exemplars are actually existent members of a category that a person has encountered in the past. Nevertheless, it involves also the similarity of an object to a standard object. Only that the standard here involves many examples and not the average, each one called an exemplar.

Again we can show the typicality effect: Objects that are similar to many examples we have encountered are classified faster to objects which are similar to few examples. You have seen a sparrow more often in your life than a penguin, so you should recognise the sparrow faster. For both prototype and exemplar approach there are experiments whose results support either one approach. Some people claim that the exemplar approach has less problems with variable categories and with atypical cases within categories. E.g. the category “games” is quite difficult to realise with the prototype approach. How do you want to find an average case for all games, like football, golf, chess. The reason for that could be that “real” category- members are used and all information of the individual exemplars, which can be useful when encountering other members later, are stored. Another point where the approaches can be compared is how well they work for differently sized categories. The exemplar approach seems to work better for smaller categories and prototypes do better for larger categories.

Some researchers concluded that people may use both approaches: When we initially learn something about a category we average seen exemplars into a prototype. It would be very bad in early learning, if we already take into account what exceptions a category has. In getting to know some of these exemplars more in detail the information becomes strengthened. “We know generally what cats are (the prototype), but we know specifically our own cat the best (an exemplar).” (Minda & Smith, 2001)

Hierarchical Organization of Categories
Now that we know about the different approaches of how we go about forming categories, let us look at the structure of a category and the relationship between categories. The basic idea is that larger categories can be split up into more specific and smaller ones.

Rosch stated that by this process three levels of categorization are created:

It is interesting that the decrease of information from basic to superordinate is really high but that the increase of information from basic down to subordinate is rather low. Scientists wanted to find out if among these levels one is preferred over the others. They asked participants to name presented objects as quickly as possible. The result was that the subjects tended to use the basic-level name, which includes the optimal amount of stored information. Therefore a picture of a retriever would be named “dog” rather than “animal” or “retriever”. It is important to note that the levels are different for each person depending on factors such as expertise and culture.

One factor which influences our categorization is knowledge itself. Experts pay more attention to specific features of objects in their area than non-experts would do. For example after presenting some pictures of birds experts of birds tend to say the subordinate name (blackbird, sparrow) while non-experts just say "bird". The basic level in the area of interest of an expert is lower than the basic level of a layperson. Therefore knowledge and experience of people affect categorization.

Another factor is culture. Imagine a people living for instance in close contact with their natural environment, and have therefore a greater knowledge about plants etc. than, for example, students in Germany. If you ask the latter what they see in nature, they use the basic level ‘tree’ and if you do the same task for the people closer to nature they will tend to answer in terms of lower level concepts such as ‘oak tree’.

Representation of Categories in the Brain
There is evidence that some areas in the brain are selective for different categories, but it is not very probable that there is a corresponding brain area for each category. Results of neurophysiological research point to a kind of double dissociation for living and non-living things. Evidence has been found in fMRI studies that they are indeed represented in different brain areas. It is important to denote that nevertheless there is much overlap between the activation of different brain areas by categories. Moreover when going one step closer into the physical area there is a connection to mental categories, too. There seem to exist neurons which respond better to objects of a particular category, namely so called “category-specific neurons”. These neurons fire not only as a response to one object but to many objects within one category. This leads to the idea that probably many neurons fire if a person recognises a particular object and that maybe these combined patterns of the firing neurons represent the object.

Semantic Networks
The "Semantic Network approach" proposes that concepts of the mind are arranged in networks, in other words, in a functional storage-system for the `meanings' of words. Of course, the concept of a semantic net is very flexible. In a graphical illustration of such a semantic net, concepts of our mental dictionary are represented by nodes, which in this way represent a piece of knowledge about our world.

The properties of a concept could be placed, or "stored", next to a node representing that concept. Links between the nodes indicate the relationship between the objects. The links can not only show that there is a relationship, they can also indicate the kind of relation by their length, for example.

Every concept in the net is in a dynamical correlation with other concepts, which may have protoypically similar characteristics or functions.

Collins and Quillian's Model

One of the first scientists who thought about structural models of human memory that could be run on a computer was Ross Quillian (1967). Together with Allan Collins, he developed the Semantic Network with related categories and with a hierarchical organisation.

In the picture on the right hand side, Collins and Quillians network with added properties at each node is shown. As already mentioned, the skeleton-nodes are interconnected by links. At the nodes, concept names are added. Like in paragraph "Hierarchical Organisation of Categories", general concepts are on the top and more particular ones at the bottom. By looking at the concept "car", one gets the information that a car has 4 wheels, has an engine, has windows, and furthermore moves around, needs fuel, is manmade.

These pieces of information must be stored somewhere. It would take too much space, if every detail must be stored at every level. So the information of a car is stored at the basis level and further information about specific cars, e.g. BMW, is stored at the lower level, where you do not need the fact that the BMW also has four wheels, if you already know that it is a car. This way of storing shared properties at a higher-level node is called Cognitive Economy.

In order not to produce redundancies, Collins and Quillian thought of this as an information inheritance principle. Information, that is shared by several concepts, is stored in the highest parent node, containing the information. So all son-nodes, that are below the information bearer, also can access the information about the properties. However, there are exceptions. Sometimes a special car has not four wheels, but three. This specific property is stored in the son-node.

The logic structure of the network is convincing, since it can show that the time of retrieving a concept and the distances in the network correlate. The correlation is proven by the sentence-verification technique. In experiments probands had to answer statements about concepts with "yes" or "no". It took actually longer to say "yes", if the concept bearing nodes were further apart.

The phenomenon that adjacent concepts are activated is called Spreading activation. These concepts are far more easily accessed by memory, they are "primed". This was studied and backed by David Meyer and Roger Schaneveldt (1971) with a lexical-decision task. Probands had to decide if word pairs were words or non-words. They were faster at finding real word pairs if the concepts of the two words were close by in the intended network.

While having the ability to explain many questions, the model has some flaws.

The Typicality Effect is one of them. It is known that "reaction times for more typical members of a category are faster than for less typical members". (MITECS) This contradicts the assumptions of Collins' and Quillian's Model, that the distance in the net is responsible for reaction time. It was experimentally determined that some properties are stored at specific nodes, therefore the cognitive economy stands in question. Furthermore, there are examples of faster concept retrieval although the distances in the network are longer.

These points led to another version of the Semantic Network approach: Collins and Loftus Model.

Collins and Loftus Model

Collins and Loftus (1975) tried to abandon these problems by using shorter or longer links depending on the relatedness and interconnections between formerly not directly linked concepts. Also the former hierarchic structure was substituted by a more individual structure of a person. Only to name a few of the extensions. As shown in the picture on the right, the new model represents interpersonal differences, such as acquired during a humans lifespan. They manifest themselves in the layout and the various lengths of the links of the same concepts.

An example: The concept "vehicle" is connected to car, truck or bus by short links, and to fire engine or ambulance with longer links.

After these enhancements, the model is so omnipotent that some researchers scarced it for being too flexible. In their opinion, the model is no longer a scientific theory, because it is not disprovable. Furthermore, we do not know how long these links are in us. How should they be measurable and could they actually?

Connectionist Approach
Every concept in a semantic net is in a dynamical correlation with other concepts which can have prototypically similar characteristics or functions. The neural networks in the brain are organised similarly. Furthermore, it is useful to include the features of ”spreading activation” and ”parallel distributed activity” in a concept of such a semantic net to explain the complexity of the very sophisticated environment.

Basic Principles of Connectionism
The connectionists did this by modeling their networks after neural networks in the nervous system. Every node of the diagram represents a neuron-like processing unit. These units can be divided into three subgroups: Input units, which become activated by a stimulation of the environment, hidden units, which receive signals from an input-unit and pass them to an output unit and output units, which show a pattern of activation that represents the initial stimulus. Excitatory and inhibitory connections between units just like synapses in the brain allow ’input’ to be analyzed and evaluated. For computing the outcome of such systems, it is useful to attach a certain ’weight’ to the input of the connectionists system, that mimics the strength of a stimulus of the human nervous system. It needs to be emphasized that connectionist networks are not models of how the nervous system works. The approach of connectionist networks is a hypothetical approach to represent categories in network patterns. Another name for the connectionist approach is Parallel Distributed Processing approach, for short PDP, since processing takes place in parallel lines and the output is distributed across many units.

Operation of Connectionist Networks
First a stimulus is presented to the input units. Then the links pass on the signal to the hidden units, that distribute the signal to the output units via further links. In the first trial, the output units shows a wrong pattern. After many repetitions, the pattern finally is correct. This is achieved by back propagation. The error signals are send back to the hidden units and the signals are reprocessed. During these repetitive trials, the ”weights” of the signal are gradually calibrated on behalf of the error signals in order to get a right output pattern at last. After having achieved a correct pattern for one stimulus, the system is ready to learn a new concept.

Evaluating Connectionism
The PDP approach is important for knowledge representation studies. It is far from perfect, but on the move to get there. The process of learning enables the system to make generalizations, because similar concepts create similar patterns. After knowing one car, the system can recognize similar patterns as other cars, or may even predict how other cars look like. Furthermore, the system is protected against total wreckage. A damage to single units will not cause the system’s total breakdown, but will delete only some patterns, which use those units. This is called graceful degradation and is often found in patients with brain lesions. These two arguments lead to the third. The PDP is organized similarly to the human brain. And some effective computer programs have been developed on this basis, that were able to predict the consequences of human brain damage.

On the other hand, the connectionist approach is not without problems. Formerly learned concepts can be superposed by new concepts. In addition, PDP can not explain more complex processes than learning concepts. Neither can it explain the phenomenon of rapid learning, which does not require extensive learning. It is assumed that rapid learning takes place in the hippocampus, and that conceptual and gradual learning is located in the cortex.

In conclusion, the PDP approach can explain some features of knowledge representation very well but fails for some complex processes.

Mental Representation
There are different theories on how living beings, especially humans encode information to knowledge. We may think of diverse mental representations of the same object. When reading the written word "car", we call this a discrete symbol. It matches with all imaginable cars and is therefore not bound to a special vehicle. It is an abstract, or amodal, representation. This is different if instead we see a picture of a car. It might be a red sports car. Now we speak of a non-discrete symbol, an imaginable picture that appears in front of our inner eye and that fits only to certain cars of sufficiently similar appearance.

Propositional Approach
The Propositional Approach is one possible way to model mental representations in the human brain. It works with discrete symbols which are strongly connected among each other. The usage of discrete symbols necessitates clear definitions of each symbol, as well as information about the syntactic rules and the context dependencies in which the symbols may be used. The symbol "car" is only comprehensible for people who do understand English and have seen a car before and therefore know what a car is about. The Propositional Approach is an explicit way to explain mental representation.

Definitions of propositions differ in the different fields of research and are still under discussion. One possibility is the following: ”Traditionally in philosophy a distinction is made between sentences and the ideas underlying those sentences, called propositions. A single proposition may be expressed by an almost unlimited number of sentences. Propositions are not atomic, however; they may be broken down into atomic concepts called ”Concepts”.

In addition, mental propositions deal with the storage, retrieval and interconnection of information as knowledge in the human brain. There is a big discussion, if the brain really works with propositions or if the brain processes its information to and from knowledge in another way or perhaps in more than one way.

Imagery Approach
One possible alternative to the Propositional Approach, is the Imagery Approach. Since here the representation of knowledge is understood as the storage of images as we see them, it is also called analogical or perceptual approach. In contrast to the Propositional Approach it works with non-discrete symbols and is modality specific. It is an implicit approach to mental representation. The picture of the sports car includes implicitly seats of any kind. If additionally mentioned that they are off-white, the image changes to a more specific one. How two non-discrete symbols are combined is not as predetermined as it is for discrete symbols. The picture of the off-white seats may exist without the red car around, as well as the red car did before without the off-white seats. The Imagery and the Propositional Approaches are also discussed in chapter 8.

Computational Knowledge Representation
Computational knowledge representation is concerned with how knowledge can be represented symbolically and how it can be manipulated in automated ways. Almost all of the theories mentioned above evolved in symbiosis with computer science. On the one hand, computer science uses the human brain as an inspiration for computational systems, on the other hand, artificial models are used to further our understanding of the biological basis of knowledge representation.

Knowledge representation is connected to many other fields related to information processing, e.g. logic, linguistics, reasoning, and the philosophical aspects of these fields. In particular, it is one of the crucial topics of Artificial Intelligence, as it deals with information encoding, storing and usage for computational models of cognition.

There are three main points that need to be addressed with regard to computational knowledge representation: The process, the formalisms and the applications of knowledge engineering.

Knowledge Engineering
The process of developing computational knowledge-based systems is called knowledge engineering. This process involves assessing the problem, developing a structure for the knowledge base and implementing actual knowledge into the knowledge base. The main task for knowledge engineers is to identify an appropriate conceptual vocabulary.

There are different kinds of knowledge, for instance rules of games, attributes of objects and temporal relations, and each type is expressed best by its own specific vocabulary. Related conceptual vocabularies that are able to describe objects and their relationships are called ontologies. These conceptual vocabularies are highly formal and each is able to express meaning in specific fields of knowledge. They are used for queries and assertions to knowledge bases and make sharing knowledge possible. In order to represent different kinds of knowledge in one framework, Jerry Hobbs (1985) proposed the principle of ontological promiscuity. Thereby several ontologies are mixed together to cover a range of different knowledge types.

A query to a system that represents knowledge about a world made of everyday items and that can perform actions in this world may look like this: “Take the cube from the table!”. This query could be processed as follows: First, since we live in a temporal world, the action needs to be a processed in a way that can be broken down into successive steps. Secondly, we make general statements about the rules for our system, for example that gravitational forces have a certain effect. Finally, we try out the chain of tasks that have to be done to take the cube from the table. 1) Reach out for the cube with the hand, 2) grab it, 3) raise the hand with the cube, etc. Logical Reasoning is the perfect tool for this task, because a logical system can also recognise if the task is possible at all.

There is a problem with the procedure described above. It is called the frame problem. The system in the example deals with changing states. The actions that take place change the environment. That is, the cube changes its place. Yet, the system does not make any propositions about the table so far. We need to make sure, that after picking up the cube from the table, the table does not change its state. It should not disappear or break down. This could happen, since the table is no longer needed. The systems tells that the cube is in the hand and omits any information about the table. In order to tackle the Frame Problem there have to be stated some special axioms or similar things. The Frame Problem has not been solved completely. There are different approaches to a resolution. Some add object spatial and temporal boundaries to the system/world (Hayes 1985). Others try more direct modeling. They do transformations on state descriptions. For example: Before the transformation the cube is on the table, after transformation, the table still exists, but independent from the cube.

Knowledge Representation Formalisms
The type of knowledge representation formalism determines how information is stored. Most knowledge representation applications are developed for a specific purpose, for example a digital map for robot navigation or a graph like account of events for visualizing stories.

Each knowledge representation formalisms needs a strict syntax, semantics and inference procedure in order to be clear and computable. Most formalisms have the following attributes to be able to express information more clearly: The Semantic Network Approach, hierarchies of concepts (e.g. vehicle -> car -> truck) and property inheritance (e.g. red cars have four wheels since cars have four wheels). There are attributes that provide the possibility to add new information to the system without creating any inconsistencies, and the possibility to create a "closed-world" assumption. For example if the information that we have gravitation on earth is omitted, the closed-world assumption must be false for our earth/world.

A problem for knowledge representation formalisms is that expressive power and deductive reasoning are mutually exclusive. If a formalism has a big expressive power, it is able to describe a wide range of (different) information, but is not able to do brilliant inferring from (given) data. Propositional logic is restricted to Horn clauses. A Horn clause is a disjunction of literals with at most one positive literal. It has a very good decision procedure(inferring), but can not express generalisations. An example is given in the logical programming language Prolog. If a formalism has a big deductive complexity, it is able to do brilliant inferring, i.e. make conclusions, but has a poor range of what it can describe. An example is second-order logic. So, the formalism has to be tailored to the application of the KR system. This is reached by compromises between expressiveness and deductive complexity. In order to get a greater deductive power, expressiveness is sacrificed and vice versa.

With the growth of the field of knowledge bases, many different standards have been developed. They all have different syntactic restrictions. To allow intertranslation, different "interchange" formalisms have been created. One example is the Knowledge Interchange Format which is basically first-order set theory plus LISP (Genesereth et al. 1992).

Applications of Knowledge Representation
Computational knowledge representation is mostly not used as a model of cognition but to make pools of information accessible, i.e. as an extension of database technology. In these cases general rules and models are not needed. With growing storage media, one is capable of creating simple knowledge bases stating all specific facts. The information is stored in the form of sentential knowledge, that is knowledge saved in form of sentences comparable to propositions and program code. Knowledge is seen as a reservoir of useful information rather than as supporting a model of cognitive activity. More recently, increased available memory size has made it feasible to use "compute-intensive" representations that simply list all the particular facts rather than stating general rules. These allow the use of statistical techniques such as Markov simulation, but seem to abandon any claim to psychological plausibility.

Artificial Intelligence
Artificial intelligence or intelligence added to a system that can be arranged in a scientific context or Artificial Intelligence (English: Artificial Intelligence or simply abbreviated AI) is defined as the intelligence of a scientific entity. This system is generally considered a computer. Intelligence is created and incorporated into a machine (computer) in order to be able to do work as human beings can. Several types of fields that use artificial intelligence include expert systems, computer games (games), fuzzy logic, artificial neural networks and robotics. Many things seem difficult for human intelligence, but for Informatics it is relatively unproblematic. For example: transforming equations, solving integral equations, making chess games or Backgammon. On the other hand, things that for humans seem to demand a little intelligence, until now are still difficult to realize in Informatics. For example: Object / Face Introduction, playing soccer.

Although AI has a strong connotation of science fiction, AI forms a very important branch of computer science, dealing with behavior, learning and intelligent adaptation in a machine. Research in AI involves making machines to automate tasks that require intelligent behavior. Examples include control, planning and scheduling, the ability to answer customer diagnoses and questions, as well as handwriting recognition, voice and face. Such things have become separate disciplines, which focus on providing solutions to real life problems. The AI system is now often used in the fields of economics, medicine, engineering and the military, as has been built in several home computer and video game software applications. This 'artificial intelligence' not only wants to understand what an intelligence system is, but also constructs it. There is no satisfactory definition for 'intelligence': 1. intelligence: the ability to acquire knowledge and use it 2. or intelligence is what is measured by a 'Intelligence Test'

Broadly speaking, AI is divided into two notions namely Conventional AI and Computational Intelligence (CI, Computational Intelligence). Conventional AI mostly involves methods now classified as machine learning, which are characterized by formalism and statistical analysis. Also known as symbolic AI, logical AI, pure AI and GOFAI, Good Old Fashioned Artificial Intelligence. The methods include: 1. Expert system: apply the capability of consideration to reach conclusions. An expert system can process a large amount of information that is known and provides conclusions based on these information. 2. Case based considerations 3. Bayesian Network 4. Behavior-based AI: a modular method for manually establishing AI systems Computational intelligence involves iterative development or learning (e.g. tuning parameters as in connectionist systems. This learning is based on empirical data and is associated with non-symbolic AI, irregular AI and soft calculations. The main methods include: 1. Neural Network: a system with very strong pattern recognition capabilities 2. Fuzzy systems: techniques for consideration under uncertainty, have been used extensively in modern industry and consumer product control systems. 3. Evolutionary computing: applying biologically inspired concepts such as population, mutation and "survival of the fittest" to produce better problem solving. These methods are mainly divided into evolutionary algorithms (e.g. genetic algorithms) and group intelligence (e.g. ant algorithms) With a hybrid intelligent system, experiments were made to combine these two groups. Expert inference rules can be generated through neural networks or production rules from statistical learning as in ACT-R. A promising new approach states that strengthening intelligence tries to achieve artificial intelligence in the process of evolutionary development as a side effect of strengthening human intelligence through technology.

History of artificial intelligence In the early 17th century, René Descartes argued that an animal's body was nothing but complicated machines. Blaise Pascal invented the first mechanical digital calculating machine in 1642. At 19, Charles Babbage and Ada Lovelace worked on programmable mechanical calculators. Bertrand Russell and Alfred North Whitehead published Principia Mathematica, which overhauled formal logic. Warren McCulloch and Walter Pitts published "Logical Calculus of Ideas that Remain in Activities" in 1943 which laid the foundation for neural networks. The 1950s were a period of active effort in AI. The first AI program to work was written in 1951 to run the Ferranti Mark I engine at the University of Manchester (UK): a script play program written by Christopher Strachey and a chess game program written by Dietrich Prinz. John McCarthy made the term "artificial intelligence" at the first conference provided for this issue, in 1956. He also discovered the Lisp programming language. Alan Turingmemper introduced "Turing test" as a way to operationalize intelligent behavior tests. Joseph Weizenbaum built ELIZA, a chatterbot that applies Rogerian psychotherapy. During the 1960s and 1970s, Joel Moses demonstrated the power of symbolic considerations to integrate problems in the Macsyma program, a knowledge-based program that was first successful in the field of mathematics. Marvin Minsky and Seymour Papert published Perceptrons, which demonstrated simple neural network boundaries and Alain Colmerauer developed the computer language Prologue. Ted Shortliffe demonstrates the power of a rule-based system for representation of knowledge and inference in diagnosis and medical therapy which is sometimes referred to as the first expert system. Hans Moravec developed the first computer controlled vehicle to deal with the tangled, starred road independently. In the 1980s, neural networks were used extensively with the reverse propagation algorithm, first explained by Paul John Werbos in 1974. In 1982, physicists such as Hopfield used statistical techniques to analyze storage properties and network optimization nerve. Psychologists, David Rumelhart and Geoff Hinton, continue their research on neural network models in memory. In 1985 at least four research groups rediscovered the Back-Propagation learning algorithm. This algorithm is successfully implemented in computer science and psychology. The 1990s marked large gains in various fields of AI and demonstrations of various applications. More specifically Deep Blue, a chess computer game, defeated Garry Kasparov in a well-known match 6 game in 1997. DARPA stated that the costs saved through applying the AI method for scheduling units in the first Gulf War had replaced all investment in AI research since 1950 to the US government. The great challenge of DARPA, which began in 2004 and continues to this day, is a race for a $ 2 million prize where vehicles are driven by themselves without communication with humans, using GPS, computers and sophisticated sensors, across several hundred miles of challenging desert areas.

Hemispheric Distribution
After having dealt with how knowledge is stored in the brain, we now turn to the question of whether the brain is specialised and, if it is specialised, which functions are located where and which knowledge is present in which hemisphere. These questions can be subsumed under the topic “hemispheric specialisation” or “lateralisation of processing” which looks at the differences in processing between the two hemispheres of the human brain.

Differences between the hemispheres can be traced back to as long as 3.5 million years ago. Evidence for this are fossils of australopithecines (which is an ancient ancestor of homo sapiens). Because differences have been present for so long and survived the selective pressure they must be useful in some way for our cognitive processes.

Differences in Anatomy and Chemistry
Although at first glance the two hemispheres look identical, they differ in in various ways.

Concerning the anatomy, some areas are larger and the tissue contains more dendritic spines in one hemisphere than in the other. An example of this is what used to be called “Broca’s area” in the left hemisphere. This area which is –among other things- important for speech production shows greater branching in the left hemisphere than in the respective right hemisphere area. Because of the left hemisphere’s importance for language, with which we will deal later, one can conclude that anatomical differences have consequences for lateralisation in function.

Neurochemistry is another domain the hemispheres differ in: The left hemisphere is dominated by the neurotransmitter dopamine, whereas the right hemisphere shows higher concentrations of norepinephrine. Theories suggest that modules specialised on cognitive processes are distributed over the brain according to the neurotransmitter needed. Thus, a cognitive function relying on dopamine would be located in the left hemisphere.

The Corpus Callosum
The two hemispheres are interconnected via the corpus callosum, the major cortical connection. With its 250 million nerve fibres it is like an Autobahn for neural data connecting the two hemispheres. There are in fact smaller connections between the hemispheres but these are little paths in comparison. All detailed higher order information must pass through the corpus callosum when being transferred from one hemisphere to the other. The transfer time, which can be measured with ERP, lies between 5 and 20 ms.

Historic Approaches
Hemispheric specialisation has been of interest since the days of Paul Broca and Karl Wernicke, who discovered the importance of the left hemisphere for speech in the 1860s. Broca examined a number of patients who could not produce speech but whose understanding of language was not severed, whereas Wernicke examined patients who suffered the opposite symptoms (i.e. who could produce speech but did not understand anything). Both Broca and Wernicke found that their patients’ brains had damage to distinct areas of the left hemisphere.

Because in these days language was seen as the cognitive process superior to all other processes, the left hemisphere was believed to be superior to the right which was expressed in the “cerebral dominance theory” developed by J.H. Jackson. The right hemisphere was seen as a “spare tire [...] having few functions of its own” (Banich, S.94). This view was not challenged until the 1930s. In this decade and the following, research dramatically changed this picture. Of special importance for showing the role of the right hemisphere was Sperry, who conducted several experiments in 1974 for which he won the Nobel Prize in Medicine and Physiology in 1981.

Experiments with Split-Brain Patients
Sperry’s experiments took place with people who suffered a condition called “split brain syndrome” because they underwent a commissurotomy. In a commissurotomy the corpus callosum is sectioned so that communication between the hemispheres becomes severed in these patients. With his pioneering experiments, Sperry wanted to find out whether the left hemisphere really plays such an important role in speech processing as suggested by Broca and Wernicke.

Sperry used different experimental designs in his studies, but the basic assumption behind all experiments of this type was that perceptual information received at one side of the body is processed in the contra-lateral hemisphere of the brain. In one of the experiments the subjects had to recognise objects by touching it with merely one hand, while being blindfolded. He then asked the patients to name the object they felt and found that people could not name it when touching it with the left hand (which is linked to the right hemisphere). The question that arose was whether this inability was due to a possible function of the right hemisphere as “spare tire” or due to something else. Sperry now changed the design of his experiment so that patients now had to show that they recognised the objects by using it the right way. For example, if they recognised a pencil they would use it to write. With this changed design, no difference in performance between both hands were found.

In a different experiment conducted by Sperry et al. the patients were shown the word sky to one visual field and scraper to the other. They now had to draw the whole word they had seen with one hand. The patients were not able to synthesise this to skyscraper, instead they draw a scraper overlapped by some cloud. Thus it was concluded that each hemisphere took control of the hand to draw what it had seen.

Experiments with Patients with other Brain-Lesions
There have been other experiments conducted to gain more knowledge about hemispheric specialisation. They were conducted with epileptic individuals who were about to receive surgery where parts of one of their hemispheres was going to be removed. Before the surgery started it was important to find out which hemisphere is responsible for speech in this individual. This was done using the Wada-technique, where barbiturate is injected into one of the arteries supplying the brain with blood. Shortly after the injection, the contra-lateral side of the body is paralysed. If the person is now still able to speak, the doped hemisphere of the brain is not responsible for speech production in this individual. With the results of this technique it could be estimated that 95\% of all adult right-handers use their left hemisphere for speech.

Research with people who suffer brain lesions or even have a commissurotomy has some major draw backs: The reason why they had to undergo such surgery is usually epileptic seizures. Because of this, it is possible that their brains are not typical or have received damage to other areas during the surgery. Also, these studies have been performed with very limited numbers of subjects, so the statistical reliability might not be high.

Experiments with Neurologically Intact Individuals
In addition to experiments with brain-severed patients, studies with neurologically intact individuals have been conducted to measure perceptual asymmetries. These are usually performed with one of three methods: Namely the “divided visual field technique”, “dichaptic presentation” and “dichotic presentation”. Each of them again has as basic assumption the fact that perceptual information received at one side of the body is processed in the contra-lateral hemisphere.

The divided visual field technique is based on the fact that the visual field can be divided into the right (RVF) and left visual field (LVF). Each visual field is processed independently from the other in the contra-lateral hemisphere. The divided visual field technique includes two different experimental designs: The experimenter can present one picture in just one of the visual fields and then let the subject respond to this stimulus. The other possibility involves showing two different pictures in each visual field.

A problem that can occur using the visual field technique is that the stimulus must be presented for less than 200 ms because this is how long the eyes can look at one point without shifting of the visual field.

In the dichaptic presentation technique the subject is presented two objects at the same time in each hand. (c.f. Sperry’s experiments)

The dichotic presentation technique enables researchers to study the processing of auditory information. Here, different information is presented simultaneously to each ear. Experiments with these techniques found that a sensory stimulus is processed 20 to 100 ms faster when it is initially directed to the specialised hemisphere for that task and the response is 10% more accurate.

Explanations for this include three hypotheses, namely the direct access theory, the callosal relay model and the activating-orienting model. The direct access theory assumes that information is processed in that hemisphere to which it is initially directed. This may result in less accurate responses, if the initial hemisphere is the unspecialised hemisphere. The Callosal relay model states that information if initially directed to the wrong hemisphere is transferred to the specialised hemisphere over the corpus callosum. This transfer is time-consuming and is the reason for loss of information during transfer. The activating-orienting model assumes that a given input activates the specialised hemisphere. This activation then places additional attention on the contra-lateral side of the activated hemisphere, “making perceptual information on that side even more salient”. (Banich)

Common Results
All the experiments mentioned above have some basic findings in common: The left hemisphere is superior at verbal tasks such as the processing of speech, speech production and recognition of letters whereas the right hemisphere excels at non-verbal tasks such as face recognition or tasks that involve spatial skills such as line orientation, or distinguishing different pitches of sound. This is evidence against the cerebral dominance theory which appointed the right hemisphere to be a spare tire! In fact both hemispheres are distinct and outclass at different tasks, and neither one can be omitted without this having high impact on cognitive performance.

Although the hemispheres are so distinct and are experts at their assigned functions, they also have limited abilities in performing the tasks for which the other hemisphere is specialised. In the picture above is an overview which hemisphere gives raise to what ability.

Differences in Processing

There are two sets of approaches to the question of hemispheric specialisation. One set of theories is about the topic by asking the question “What tasks is each hemisphere specialised for?”. Theories that belong to this set, assign the different levels of ability to process sensory information to the different levels of abilities for higher cognitive skills. One theory that belongs to this set is the “spatial frequency hypothesis”. This hypothesis states that the left hemisphere is important for fine detail analysis and high spatial frequency in visual images whereas the right hemisphere is important for low spatial frequency. We have pursued this approach above.

The other approach does not focus on what type of information is processed by each hemisphere but rather on how each hemisphere processes information. This set of theories assumes that the left hemisphere processes information in an analytic, detail- and function-focused way and that it places more importance on temporal relations between information, whereas the right hemisphere is believed to go about the processing of information in a holistic way, focusing on spatial relations and on appearance rather than on function.

The picture above shows an exemplary response to different target stimuli in an experiment on global and local processing with patients who suffer right- or left-hemisphere damage. Patients with damage to the right hemisphere often suffer a lack of attention to the global form, but recognise details with no problem. For patients with left-hemisphere-damage this is true the other way around. This experiment supports the assumption that the hemispheres differ in the way they process information.

Interaction of the Hemispheres
Why is the transfer between the hemispheres needed at all if the hemispheres are so distinct concerning functioning, anatomy, chemistry and the transfer results in degrading of quality of information and takes time? The reason is that the hemispheres, although so different, do interact. This interaction has important advantages because as studies by Banich and Belger have shown it may “enhance the overall processing capacity under high demand conditions” (Banich). (Under low demand conditions the transfer does not make as much sense because the cost of transferring the information to the other hemisphere are higher than the advantages of parallel processing.)

The two hemispheres can interact over the corpus callosum in different ways. This is measured by first computing performance of each hemisphere individually and then measuring the overall performance of the whole brain. In some tasks one hemisphere may dominate the other in the overall performance, so the overall performance is as good or bad as the performance of one of the single hemispheres. What's surprising is that the dominating hemisphere may very well be the one that is less specialised, so here is another example of a situation where parallel processing is less effective than processing in just one half of the brain.

Another way of how the hemispheres interact is that overall processing is an average of performance of the two individual hemispheres.

The third, most surprising way the hemispheres can interact is that when performing a task together the hemispheres behave totally different than when performing the same task individually. This can be compared to social behavior of people: Individuals behave different in groups than they would when being by themselves.

Individual Factors Influencing Lateralisation
After having looked at hemispheric specialisation from a general point of view, we now want to focus on differences between individuals concerning hemispheric specialisation. Aspects that may have an impact on lateralisation might be age, gender or handed-ness.

Age could be one factor which decides in how far each hemisphere is used at specific tasks. Researchers have suggested that lateralisation develops with age until puberty. Thus infants should not have functionally-lateralised brains. Here are four pieces of evidence that speak against this hypothesis:

Infants already show the same brain anatomy as adults. This means the brain of a new born is already lateralised. Following the hypothesis that anatomy is linked to function this means that lateralisation is not developed at a later period in life.

Differences in perceptual asymmetries that means superior performance at processing verbal vs. non- verbal material in the different hemispheres cannot be observed in children aged 5 to 13, i.e. children aged 5 process the material the same way 13 year olds do.

Experiments with 1-week-old infants showed that they responded with increased interest to verbal material when this was presented to the right ear than when presented to the left ear and increased interest to non-verbal material when presented to the left ear. The infants’ interest was hereby measured by the frequency of soother sucking.

Although children who underwent hemispherectomy (the surgical removal of one hemisphere) do develop the cognitive skills of the missing hemisphere (in contrast to adults or adolescents who can only partly compensate for missing brain parts), they do not develop these skills to the same extent as a child with hemispherectomy of the other hemisphere. For example: A child whose right hemisphere has been removed will develop spatial skills but not to the extent that a child whose left hemisphere has been removed, and thus still possesses the right hemisphere.

Handedness is another factor that might influence brain lateralisation. There is statistical evidence that left-handers have a different brain organisation than right-handers. 10% of the population is left-handed. Whereas 95% of the right-handed people process verbal material in a superior manner in the left-hemisphere, there is no such a high figure for verbal superiority of one hemisphere in left-handers: 70% of the left-handers process verbal material in the left-hemisphere, 15% process verbal material in the right hemisphere (the functions of the hemispheres are simply switched around), and the remaining 15% are not lateralised, meaning that they process language in both hemispheres. Thus as a group, left-handers seem to be less lateralised. However a single left-handed-individual can be just as lateralised as the average right-hander.

Gender is also an aspect that is believed to have impact on the hemispheric specialisation. In animal studies, it was found that hormones create brain differences between the genders that are related to reproductional functions. In humans it is hard to determine to which extent it is really hormones that cause differences and to which extent it is culture and schooling that are responsible.

One brain area for which a difference between the genders was observed is the corpus callosum. Although one study found that the c.c. is larger in women than in men these results could not be replicated. Instead it was found that the posterior part of the c.c. is more bulbous in women than in men. This might however be related to the fact that the average woman has a smaller brain than the average man and thus the bulbousness of the posterior section of the c.c. might be related to brain size and not to gender.

In experiments that measure performance in various tasks between the genders the cultural aspect is of great importance because men and women might use different problem solving strategies due to schooling.

Summary
Although the two hemispheres look like each other’s mirror images at first glance, this impression is misleading. Taking a closer look, the hemispheres not only differ in their conformation and chemistry, but most importantly in their function. Although both hemispheres can perform all basic cognitive tasks, there exists a specialisation for specific cognitive demands. In most people, the left hemisphere is an expert at verbal tasks, whereas the right hemisphere has superior abilities in non-verbal tasks. Despite the functional distinctness the hemispheres communicate with each other via the corpus callosum.

This fact has been utilised by Sperry’s experiments with split-brain-patients. These are outstanding among other experiments measuring perceptual asymmetries because they were the first experiments to refute the hemispheric dominance theory and received recognition through the Nobel Prize for Medicine and Physiology.

Individual factors such as age, gender or handed-ness have no or very little impact on hemispheric functioning.

Knowledge Representation
Goldstein, E. Bruce.(2005). Cognitive Psychology - Connecting, Mind Research, and Everyday Experience. Thomson, Wadsworth. Ch 8 Knowledge, 265-308.

Sowa, John F.(2000). Knowledge Representation - Logical, Philosophical, and Computational Foundations. Brooks/Cole.

Slides concerning Knowledge from: http://www.cogpsy.uos.de/, Knowledge: Propositions and images. Knowledge: Concepts and categories.

Minda, J. P. & Smith, J. D. (2001). Prototypes in category learning: The effects of category size, category structure, and stimulus complexity. Journal of Experimental Psychology: Learning, Memory, & Cognition, 27, 775–799.

Hemispheric Distribution
Banich, Marie T.(1997).Neuropsycology - The Neural Bases of Mental Function. Hougthon Mifflin Company. Ch 3 Hemispheric Specialisation, 90-123.

Hutsler, J. J., Gillespie, M. E., and Gazzaniga (2002). The evolution of hemispheric specialisation. In Bizzi, E., Caliassano, P. and Volterra V. (Eds.) Frontiers of Life, Volume III: The Intelligent Systems Academic Press: New York.

Birbaumer, Schmidt(1996). Biologische Psychologie. Springer Verlag Berlin-Heidelberg. 3. Auflage. Ch 24 Plastizität, Lernen, Gedächtnis. Ch 27 Kognitive Prozesse (Denken).

Kandel, Eric R.; Schwartz, James H.; Jessel, Thomas M.(2000). Principles of Neural Science. Mc Graw Hill. 4.th edition. Part IX, Ch 62 Learning and Memory.

Ivanov, Vjaceslav V.(1983). Gerade und Ungerade - Die Assymmetrie des Gehirns und der Zeichensysteme. S.Hirzel Verlag Stuttgart.

David W.Green ; et al.(1996). Cognitive Science - An Introduction. Blackwell Publishers Ltd. Ch 10 Learning and Memory(David Shanks).

Knowledge Representation
From Stanford Encyclopedia of Philosophy: knowledge analysis, knowledge by acquaintance and knowledge by description

Lecture on Knowledge and Reasoning, University of Erlangen Germany

Links to Knowledge-Base and Ontology Projects Worldwide

Links on Ontologies and Related Subjects

Knowledge Representation: Logical, Philosophical, and Computational Foundations, by Sowa, John F.

Hemispheric Distribution
Evolution of Hemispheric Specialisation, by Hutsler, Gillespie, Gazzaniga

Cerebral specialisation and interhemispheric communication, by Gazzaniga,in Oxford Journals


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User talk:Rorylg

Blank page Talk:A-level Chemistry/OCR/Foundation/Group II Elements and Their Compounds
I noticed that you blanked the page. I understand that the page was in the wrong place, but blank pages are not acceptable at Wikibooks.

You probably want to delete the page. Is that what you want to do? You can mark a page with , or because I am an administrator, you could just ask me to delete it. --Kernigh 22:25, 12 April 2006 (UTC)

Hi. I was perusing the chemistry section and noticed that it was rather empty so I added the piece (my first) but, as you pointed out, it was in the wrong place. I have moved it (I think) to the section on stoichiometry because it forms a basis for further development in this area. Rorylg 22:32, 12 April 2006 (UTC)

title: User talk:Rorylg


text: Category:Venezuelan recipes

title: Category:Venezuelan recipes


text:
D Programming/Interfaces

There is an article in wikipedia describing the concept of interface in common.

The corresponding D specification is found on digitalmars.

An interface is something like a promise. A class implementing an interface promises to have at least all the listed methods. A class reference can be casted to the type of an implemented interface. This is useful, if another component is only interested in a certain aspect of an object.

Interface Declaration
interface Identifier {  //interface body begins here. }  //interface body ends here.

title: D Programming/Interfaces


text:
D Programming/Embedding D in HTML

The dmd compiler has the capability to read an .html file as source. Every code between ... is treated as D code, and dmd will compile it.

title: D Programming/Embedding D in HTML


text:
D Programming/Documentation comments

The -D option to the dmd activates the generation of Ddoc. This means, special comments in the code are used to write a HTML file. The advantage of code-comments are:
 * code and documentation are near at each other. Better chance to be consistent
 * easy to use

dmd generates documentation per module.

CandyDoc
Enhancement to DDoc, to have an navigation. It can be found on the helix website.

title: D Programming/Documentation comments