David Poza
email: djpoza@gmail.com

José Manuel Galán

José Ignacio Santos
email: jisantos@ubu.es

Adolfo López-Paredes
email: adolfo@insisoc.org

An Agent Based Model of the Nash Demand Game in Regular Lattices

Keywords: Agent-based modeling, Nash demand game, game theory, negotiation, segregation, tags, social norms

 including the spatial dimension in the game. Each agent is endowed with memory and plays the best reply against the opponent's most frequent demand. The results show that all the possible persistent regimes of the global interaction game can also be obtained with this spatial version. Our preliminary analysis also suggests that the topological distribution of the agents can generate new persistent regimes within groups of agents with the same tag.

Introduction

The gist of agent-based modeling consists in the individual abstraction of the entities that participate in a given target system as computational entities -agents -in a model [START_REF] Galán | Errors and artefacts in agent-based modelling[END_REF], [START_REF] Izquierdo | Modelado de sistemas complejos mediante simulación basada en agentes y mediante dinámica de sistemas[END_REF]. It also facilitates to establish a direct correspondence between the interactions observed as agent"s interactions, and hence to analyze the emerging behavior.

In the last twenty years, this technique has become popular in almost every scientific domain, such as natural resources management [START_REF] López-Paredes | Agent-based modelling in Natural Resource Management[END_REF], biology [START_REF] Walker | Agent-based computational modelling of wounded epithelial cell monolayers[END_REF], project management [START_REF] Araúzo-Araúzo | Gestión eficiente de carteras de proyectos. Propuesta de un sistema inteligente de soporte a la decisión para oficinas técnicas y empresas consultoras[END_REF], etc. Nonetheless, it has been particularly useful in economic and social systems as a formalization tool.

The role that norms play as regulator mechanisms of certain aspects of social, economic and organizational behaviors has been thoroughly studied in social sciences [START_REF] Coleman | Foundations of social theory[END_REF], and even by means of agent-based modeling [START_REF] Galan | Appearances Can Be Deceiving: Lessons Learned Re-Implementing Axelrod's 'Evolutionary Approach to Norms[END_REF]. Once a norm has been established, it acts as a self-reinforcement mechanism of behavior. However, the emergence of norms is, in general, exempt from explicit mechanisms of control. This is a key factor to understand the informal behavior of the organizations.

In this work, we analyze the emergence of norms using the agent-based model proposed by [START_REF] Axtell | The Emergence of Classes in a Multi-Agent Bargaining Model[END_REF], AEY"s model from now on, which is an extension of the well known Nash demand game. By studying the transient and the asymptotic dynamics of this model, Axtell et al proved that self-reinforced norms can emerge spontaneously. These emergent norms may be completely different from one another even though all the agents of the population have the same behavior rule. Moreover, when agents are endowed with a tag (which initially has no meaning) and remember the past behaviors of their opponents and their tags, it is possible to demonstrate that segregation, in terms of different behavior within and outside a group, can emerge endogenously.

We have generalized the original model by adding a new behavior rule that requires less cognitive abilities than those required in the original paper. Furthermore, when agents used this new behavior rule, the segregation emerged even more frequently. In our analysis, we have also incorporated the influence of the topology on the results of the game. To this aim, we have considered the spatial dimension of the game by introducing a regular spatial structure.

This work is organized as follows: first, we will briefly explain the extensions and modifications that we have performed to generalize the AEY original model. Next, we will describe the results that we have obtained when agents are randomly distributed. Afterwards, we will discuss some cases where several persistent regimes can simultaneously emerge and the possible relation with mesoscopic topological effects. We will finish with conclusions and extensions of this work.

AEY's Model in Regular Spatial Structures

In AEY"s model there is a population of n agents that repetitively play a bargaining game. The interaction process in the model consists in the following: two agentsrandomly selected from the population of agents -demand some portion of a pie (which is a metaphor of something that is going to be shared between two persons). The portion of the pie that each agent gets depends on the corresponding portion demanded by her opponent: they get what they demand as long as the sum of both demands is not higher than the whole of the pie; otherwise both get nothing.

In order to simplify the analysis we will assume that agents have only three possible demands: low (30%), medium (50%) and high (70%). Table 1 shows one agent"s reward depending on her opponent"s demand - [START_REF] Poza | Impact of Tag Recognition in Economic Decisions[END_REF] study the influence of payoffs in the game-. Agents have a memory in which they keep the latest m decisions taken by their opponents in previous games (where m is the memory length). Agents check their memory and take their decision depending on the strategies followed by their opponents in the previous matches. When the game starts, the agents" memories are initialized with m random values between the three options they can demand (low, medium or high).

There is a small probability of a player not following the rational decision rule and playing a random strategy instead, called the mutation probability ε. In the most sophisticated version of the model, each agent can be identified as a member of one of the two possible groups by means of a tag: agents store in their memory their opponents" demands conditioned by the corresponding group.

In our simulation model, agents take decisions by using two different decision rules: either maximizing the expected utility in function of the memory vector, or demanding the best reply against the opponent"s most frequent demand.

In the original model by AEY, players randomly paired at each tick could play with any other in the population. However, in our spatial version of the model where agents are placed on a 10x10 toroidal reticule, each one interacts with one of her eight spatial neighbours located in a radius-1 Moore neighbourhood.

A simulation run stops when the system reaches a persistent regimen. As there are no absorbent states for ε>0, we consider that a persistent regimen has been reached either when all the agents have at least (1-ε)•m instances of "medium" behaviour in their memories (an equitable norm) or when there are, at most, ε•m instances of "medium" behaviour in their memories (a non-equitable norm). When tags are considered in the game, the stop criterion is applied to both agents" memories corresponding to same and different tagged opponents.

Equilibrium in a random scenario

We have studied the model in different scenarios. In the first one, tagged agents are randomly distributed in the grid (see Figure 1).

The system is an ergodic time-homogeneous Markov chain [START_REF] Izquierdo | Techniques to understand computer simulations: Markov chain analysis[END_REF]. Although there is a strictly positive probability of finding the system in each of its states in the long run, some states are more persistent than others -the probability of staying in them is higher-. The best way of representing the state space is a simplex of the agents' memory states. Because the memory of an agent is made by two partitions, corresponding to the past demands of the two classes of opponents, we use two simplexes to represent both ones.

Fig. 1.

A sample of random distribution of agents in the grid. In order to show clearly the players' strategies for both kind s of contacts, two symmetric grids are used to represent interactions between agents with the same tag (the left one) and with different tag (the right one), called "intratype" and "intertype" interactions respectively.

The pure strategy equilibrium MM of the Nash demand game, or equity norm, is a stochastically stable equilibrium for mutation rates small enough -once it is reached, it is very unlikely the system leaves it-. However, other regions of inequality strategies can be considered as pseudo-stable because the transition time to go from them to the equity norm is enormously long, and it grows exponentially with the number of agents and the memory length m, so the system can permanence in them over a long time.

Fig. 2. Most probable states (persistent regimens) in the intertype game

Figure 2 depicts two important states of the memory of agents playing against opponents of different tag -intertype game-On the left simplex, everyone plays the strategy one-half, whereas on the right one every agent of one group (circles in the example) demands high against agents of the other group (squares), who consequently demand low.

Fig. 3. Most probable states in the intratype game

Figure 3 shows the three possible final states of the intratype interactions. In the first one (left), equity emerges within two groups (circles and squares). The second one (middle) represents a fractious state where both groups have members demanding simultaneously low and high. And finally, the third state (right) depicts a segregated situation in which equity emerges within one group (circles) whereas the other group (squares) plays inequality strategies.

Other persistent regimes

The results commented in the previous section confirm that the same six combinations of intertype and intratype persistent regimes pertaining to the global interaction game can also be obtained in the regular lattice game. Notwithstanding, when we run random intensive simulations in the spatial game we find that there is someone that needs a much higher time to converge to one of the mentioned regimens than the average time. This fact could suggest the appearance of some other basins of attraction beyond the mentioned combinations of states. This might imply that there are more situations where the transient dynamics of the system differs from the long run behavior of the system. A deeper insight into the tag spatial distribution puts forward two different types of situations (see Figure 4). A first case occurs when the particular distribution of tagged agents in the grid results in isolated areas of players with the same tag, without any contact between them. The behavior of disconnected frontiers of agents of the same type can produce different persistent regimens in each frontier. An extreme case of this effect is represented in Figure 4.

However, the other kind of situations is much more interesting. Now, the system state is characterized by non-disconnected clusters of agents -with the same tagplaying simultaneously different types of intertype coordination. The key difference with the previous case is the existence of an interconnection between the clusters.

The analyzed cases suggest that this last regime only appears when there is a strong community structure in the underlying network topology of the players, and that each regime corresponds to a different community. Our hypothesis is that the regimes obtained in the regular lattice version of the game are influenced by a mesoscale dimension between the individual and the complete population. In order to verify this intuition we have studied the community structure of an idealized case when this phenomenon frequently appears (see Figure 5). In order to find out the community structure of the network, we have used the Girvan-Newman algorithm [START_REF] Girvan | Community structure in social and biological networks[END_REF] based on the iterative removal of the nodes with more betweenness. Our results depicted in Figure 6 show that, as predicted, the different regimes correspond to the partition of the network in communities detected by the algorithm. Fig. 6. On the left of the picture we can observe two different simultaneous persistent regimes in a connected population of agents with the same tag (h-shaped area). The different regimes correspond exactly to the different communities obtained in the underlying network of neighbors.

Conclusions

In this work we have analyzed the Nash demand game played by a finite population of tagged agents situated in a regular lattice and playing as decision rule the best reply against the opponent's most frequent demand. Our results confirm that all the different regimes obtained in the global interaction model, including discriminatory norms, can emerge. Furthermore, computational simulations show that different persistent regimes can appear simultaneously within a group of agents with the same tag depending on the topology -especially the mesoscopic characteristics of the network-. Some results correspond to the trivial case, when there are disconnected clusters of players with the same tag, but also to other non-evident distributions of tagged agents in the grid. A preliminary study suggests that the community structure of the network of players with the same tag could be a strong predictor of these special regimes of the spatial version of the Nash demand game.

Fig. 4 .

 4 Fig. 4. The intertype plot (right) shows two different persistent regimens: one LH at the center and another MM at the right and left borders. Notice that the lattice is a torus.

Fig. 5 .

 5 Fig. 5. We have analyzed the effects that appear in the stylized configuration showed on the left of the figure. On the right we represent the underlying interaction structure for the intratype game of grey-tagged players.

Table 1 .

 1 Payoff matrix

		H	M	L
	H	0,0	0,0	70,30
	M	0,0	50,50	50,30
	L	30,70	30,50	30,30

Acknowledgments.

The authors have benefited from the financial support of the Spanish Ministry of Science and Technology (project TIN2008-06464-C03-02), of the Junta de Castilla y León (project BU034A08) and Caja Burgos (project 2009/00148/001 and project 2009/00199/001).


Sara Rodríguez

Dante I Tapia
email: dantetapia@usal.es

Eladio Sanz
email: esanz@usal.es

Carolina Zato
email: carol_zato@usal.es

Fernando De La Prieta

Oscar Gil
email: oscar.gil@usal.es

Cloud Computing Integrated into Service-Oriented Multi-Agent Architecture

Keywords: Cloud Computing, Multi-Agent Architecture, SOA

The main objective of Cloud Computing is to provide software, services and computing infrastructures carried out independently by the network. This concept is based on the development of dynamic, distributed and scalable software. In this way there are Service-Oriented Architectures (SOA) and agent frameworks which provide tools for developing distributed systems and multi-agent systems that can be used for the establishment of cloud computing environments. This paper presents CISM@ (Cloud computing Integrated into Service-Oriented Multi-Agent) architecture set on top of the platforms and frameworks by adding new layers for integrating a SOA and Cloud Computing approach and facilitating the distribution and management of functionalities.

Introduction

The main objective of Cloud Computing is for the network to independently provide software, services and computing infrastructure. This concept is based on the development of dynamic, distributed and scalable software. These types of software usually require the creation of increasingly complex and flexible applications, so there is a trend toward reusing resources and sharing compatible platforms or architectures. In some cases, applications require similar functionalities already implemented into other systems, which are not always compatible.

For this reason, it is necessary to develop new functional architectures capable of providing adaptable and compatible frameworks and allowing access to services and applications regardless of time and location restrictions. There are Service-Oriented Architectures (SOA) and agent frameworks [START_REF] Maamar | Toward an Agent-Based and Context-Oriented Approach for Web Services Composition[END_REF], [START_REF] Buhler | Integrating Agent Services into BPEL4WS Defined Workflows[END_REF], [START_REF] Fuentes-Fernández | Integration of Web Services in an Agent-Oriented Methodology[END_REF], which provide tools for developing distributed systems and multi-agent systems [START_REF] Martin | The Open Agent Architecture: A framework for Building Distributed Software Systems[END_REF], [START_REF] Sycara | The RETSINA MAS Infrastructure[END_REF], [START_REF] Bellifemine | JADE-A FIPA-compliant Agent Framework[END_REF] that can be used for the establishment of cloud computing environments. However, these tools do not solve the development requirements of these systems by themselves. Therefore, it is necessary to develop innovative solutions that integrate different approaches in order to create flexible and adaptable systems, especially for achieving higher levels of interaction with people in a ubiquitous and intelligent way.

One of the most prevalent alternatives in distributed architectures is agent and multi-agent systems which can help to distribute resources and reduce the central unit tasks [START_REF] Ardissono | A Conversational Approach to the Interaction with Web Services[END_REF]. CISM@ (Cloud Computing Integrated on Service-oriented Multi-Agent) architecture is set on top of the platforms and frameworks by adding new layers for integrating a SOA and Cloud Computing approach and facilitating the distribution and management of functionalities. A distributed agent-based architecture provides more flexible ways to move functions to where actions are needed. Additionally, the programming effort is reduced because it is only necessary to specify global objectives so that agents cooperate in solving problems and reaching specific goals, thus giving the systems the ability to generate knowledge and experience. Unfortunately, the difficulty in developing a multi-agent architecture lies in the need of having more complex analysis and design stages, which implies more time to reach the implementation. Moreover, the system control is reduced because the agents need more autonomy to solve complex problems.

The main purpose of this research is to design and implement an architecture that is applicable to the development of intelligent highly dynamic environments. The architecture proposes several features capable of being executed in dynamic and distributed environments to provide interoperability in a standard framework. These features can be implemented in devices with limited storage and processing capabilities. CISM@ integrates intelligent agents with a service-oriented philosophy.

In the next section, the problem that has motivated the development of the CISM@ architecture, combining concepts of Cloud Computing, SOA and Multi-Agent, systems will be explained. The specific characteristics and the agent-based architecture will be described in section 3. Finally, section 4 will present the results and the conclusions obtained.

Problem Description

Distributed architectures are aimed at achieving interoperability between different systems, distribution of resources, and the lack of dependency of programming languages [START_REF] Cerami | Web Services Essentials: Distributed Applications with XML-RPC, SOAP, UDDI & WSDL[END_REF]. Functionalities are linked by means of standard communication protocols that must be used by applications in order to share resources in the network [START_REF] Ardissono | A Conversational Approach to the Interaction with Web Services[END_REF]. The compatibility and management of messages between functionalities is an important and complex element in any of these approaches.

Agent and multi-agent systems (MAS) combine classical and modern functional architecture aspects. We have that an agent is anything with the ability to perceive its environment through sensors and respond in the same environment through actuators, assuming that each agent may perceive its own actions and learn from the experience [START_REF] Russell | Artificial Intelligence: a Modern Approach[END_REF]. MAS is defined as any system composed of multiple autonomous agents with incomplete capabilities to solve a global problem, where there is no global control system, the data is decentralized and the computing is asynchronous) [START_REF] Wooldridge | An Introduction to MultiAgent Systems[END_REF].

Multi-agent architectures and frameworks such as Open Agent Architecture (OAA) [START_REF] Martin | The Open Agent Architecture: A framework for Building Distributed Software Systems[END_REF], RETSINA [START_REF] Sycara | The RETSINA MAS Infrastructure[END_REF] and JADE [START_REF] Bellifemine | JADE-A FIPA-compliant Agent Framework[END_REF] define agent-based structures to resolve distributed computational problems and facilitate user interactions. Nevertheless, integration is not always achieved because of two reasons. The first is the incompatibility between the agent platforms. To resolve this, two alternatives are possible. One of them is centered on the communication between the different models of the platform [START_REF] Bonino Da Silva | A Service Architecture for Context Awareness and Reaction Provisioning[END_REF] and the other is focused on the integration of distributed services in the agent infrastructure [START_REF] Ricci | An agent-oriented programming model for SOA & web services[END_REF]. The second reason is that Cloud-based architectures usually do not provide intelligent computational and interactive mechanisms. CISM@ combines both paradigms, trying to take advantage of their strengths and avoid their weaknesses.

Although these developments provide an adequate background for developing distributed MAS with a cloud computing approach based on Web Services, most of them are in early stages of development, so it is not actually possible to know their potential in real scenarios. In addition, CISM@ not only provides communication and integration between distributed agents, services and applications; it also proposes a new method for facilitating the development of distributed MAS by means of modeling the functionalities of the agents and the systems as services and applications based on cloud computing.

Cloud Computing is an innovative concept, which is based on the compendium of a group of technologies. These proportionate a computational paradigm to offer services in three different levels: IaaS, PaaS y SaaS [START_REF] Foste | Cloud Computing and Grid Computing 360-Degree Compared[END_REF], [START_REF] Vaquero | A break in the Clouds: Towards a Cloud Definition[END_REF], [START_REF] Gruman | What Cloud Computing really means. Info World[END_REF].

The IaaS (Infrastructure as a Service) level is oriented to provide hardware and/or software equipment (operating system, process capacity, storage, etc.) as a technology environment in different developments. The capacity to vary under demand is also included in the characteristics of the services [START_REF]S3 Amazon[END_REF], [START_REF] Varia | Amazon white paper on cloud architectures[END_REF]. The PaaS (Platform as a Service) level offers integration but with higher abstraction, allowing the construction of customized services by different services [START_REF]Google App Engine[END_REF]. The SaaS (Software as a Service) level offers software with a specific purpose that is totally functional and available through the Internet.

Although the concept of providing services at three different levels is clearly accepted, the needed standards and interfaces are currently still not defined and therefore, the interoperability between different Cloud Computing platforms is an open problem today [START_REF] Russell | Artificial Intelligence: a Modern Approach[END_REF]. CISM@ aims to solve these problems, by specifying a welldefined architecture that integrates the main frameworks of intelligent agents in order to provide Cloud Computing services, both at software (SaaS) and platform (PaaS) level.

CISMA@ allows the development of MAS with increased scalability and reutilization of resources. In addition, CISMA@ allows the extraction and modeling of the agents functionalities as independent services and applications, giving as a result, lighter agents in terms of computational processing. A distributed approach provides the architecture with a greater capacity for recovery from errors, and thus, a greater flexibility to adjust its behavior in execution time.

CISM@ Architecture

CISM@ is a novel architecture which integrates a cloud computing approach with SOA and intelligent agents for building systems that need be dynamic, flexible, robust, adaptable to changes in context, scalable and easy to use and maintain. The architecture proposes a new and easier method to develop distributed intelligent systems, where cloud services can communicate in a distributed way with intelligent agents, even from mobile devices, independent of time and location restrictions. The architecture focuses on distributing the majority of the systems' functionalities into remote and local services and applications. The functionalities of the systems are not integrated into the structure of the agents; rather they are modeled as distributed services and applications that are invoked by the agents acting as controllers and coordinators. Because the architecture acts as an interpreter, the users can run applications and services programmed in virtually any language, but have to follow a communication protocol that every cloud service must incorporate. Another important functionality is that, thanks to the agents' capabilities, the systems developed can make use of reasoning mechanisms or learning techniques to handle cloud services according to context characteristics, which can change dynamically over time. Agents and cloud services can communicate in a distributed way, even from mobile devices. This makes it possible to use resources no matter its location. It also allows the starting or stopping of agents, services or devices separately, without affecting the rest of resources, so the system has an elevated adaptability and capacity for error recovery.

CISM@ is based on agents because of their characteristics, such as autonomy, reasoning, reactivity, social abilities, pro-activity, mobility, organization, etc., which allow them to cover several needs for highly dynamic environments, especially ubiquitous communication and computing and adaptable interfaces. CISM@ combines a cloud computing approach built on top of Web Services and intelligent agents to obtain an innovative architecture, facilitating ubiquitous computation and communication and high levels of human-system-environment interaction. It also provides an advanced flexibility and customization to easily add, modify or remove services on demand, independently of the programming language. The goal in CISM@ is not only to distribute services, but also to promote a new way of developing highly dynamic systems focusing on ubiquity and simplicity. CISM@ provides a flexible distribution of resources and facilitates the inclusion of new functionalities in highly dynamic environments based on Cloud Computing concept. It also provides the systems with a higher ability to recover from errors and a better flexibility to change their behavior at execution time.

CISM@ sets on top of existing agent frameworks by adding new layers to integrate a cloud computing approach and facilitate the provision and management of services at two different levels, Software as a Service (SaaS) and Platform as a Service (PaaS) [START_REF] Foste | Cloud Computing and Grid Computing 360-Degree Compared[END_REF]. Therefore, the CISM@ framework has been modeled following the Cloud Computing model based on SOA, but adding the applications block which represents the interaction with users. These blocks provide all the functionalities of the architecture. CISM@ adds new features to common agent frameworks, such as OAA, RETSINA and JADE and improves the services provided by these previous architectures. These previous architectures have limited communication abilities.

Fig. 1. CISM@ Architecture over Different Agent Platforms

As can be seen in Figure 1, CISM@ defines four basic blocks:

1. PaaS (Platform as a Service). This involves all the custom applications that can be used to exploit the system functionalities. Applications are dynamic and adaptable to context, reacting differently according to the particular situation. They can be executed locally or remotely, even on mobile devices with limited processing capabilities, because computing tasks are largely delegated to the agents and services. 2. Agent Platform. This is the core of CISM@, integrating a set of agents, each one with special characteristics and behavior. The agents act as controllers and administrators for all cloud services, managing the adequate functioning of the system. In CISM@, services are managed and coordinated by deliberative BDI agents with distributed computation and coordination abilities. 3. SaaS (Software as a Service). These represent the activities that the architecture offers. Services are designed to be invoked locally or remotely and they can be organized as local services, Web Services, Cloud services, or even as individual stand-alone services. Services can make use of other services to provide the functionalities that users require. CISM@ has a flexible and scalable directory of services, so they can be invoked, modified, added, or eliminated dynamically and on demand. 4. Communication Protocol. This allows applications and services to communicate directly with the agent platform. The protocol is completely open and independent of any programming language, facilitating ubiquitous communication capabilities. This protocol is based on SOAP specification to capture all messages between the platform and the services and applications [START_REF] Cerami | Web Services Essentials: Distributed Applications with XML-RPC, SOAP, UDDI & WSDL[END_REF]. All external communications follow the same protocol, while the communication amongst agents in the platform follows the FIPA Agent Communication Language (ACL) specification.

One of the advantages of CISMA@ is that the users can access the system through distributed applications, which run on different types of devices and interfaces (e.g. computers, cell phones, PDAs). All requests and responses are handled by the agents in the platform. The agents analyze all requests and invoke the specified services either locally or remotely. Services process the requests and execute the specified tasks. Then, the services send back a response with the result of the specific task.

A simple case can better demonstrate the basic functioning of CISM@ when it is requesting a service. A user needs to calculate the sum of two numbers and wants to do it through a mobile device (e.g. PDA) remotely connected to the system. The user executes a mathematical toolkit, which provides him with a large set of formulas from which he selects the sum function, introduces a set of values, and clicks a button to get the result. When the user clicks the button, the application sends a request to the platform to find a service that can process that request. The agents invoke the appropriate service and send the request. The service processes the request and sends the result back to the agents, which in turn, send it to the application. It is obvious that invoking a remote service to execute a sum is not the best choice. But imagine a large-scale process that uses complex AI (Artificial Intelligence) techniques, such as genetic algorithms, data mining, neural networks, etc. where the limited processing capacity of the mobile device makes it impossible to calculate. In this case, the service may be in a powerful computer and could be remotely invoked by the mobile device.

The Web Services Architecture model uses an external directory, known as UDDI (Universal Description, Discovery and Integration), to list all available services. Each service must send a WSDL (Web Services Description Language) file to the UDDI to be added to the directory. Applications consult the UDDI to find a specific service. Once the service is located, the application can start communication directly with the selected service. However, CISM@ does not include a service discovery mechanism, so applications must use only the services listed in the platform. In addition, all communication is handled by the platform, so there is no way to interact directly between applications and services. Moreover, the platform makes use of deliberative agents to select the optimal option to perform a task, so users do not need to find and specify the service to be invoked by the application. These features have been introduced in CISM@ to create a secure communication between applications and services. They also facilitate the inclusion of new services regarding their location and application that users can make use of.

CISM@ is a modular multi-agent architecture, where services and applications are managed and controlled by deliberative BDI agents. There are different kinds of agents in the architecture, each one with specific roles, capabilities and characteristics. This fact facilitates the flexibility of the architecture when incorporating new agents. However, there are pre-defined agents, which provide the basic functionalities of the architecture.

The CISM@ pre-defined agents are described next: 1. PaaS Agent. This agent is responsible for all communications between applications and the agent platform. It manages the incoming requests from the applications to be processed by services. It also manages responses from services (via the platform) to applications. All messages are sent to Security Agent for their structure and syntax to be analyzed.

2. SaaS Agent. This agent is responsible for all communications between services and the agent platform. The functionalities are similar to PaaS Agent but backwards. All messages are sent to Security Agent for their structure and syntax to be analyzed. This agent also periodically checks the status of all services to know if they are idle, busy, or crashed. 3. ServiceDir Agent. This agent manages the list of services that can be used by the system. For security reasons, the list of services is static and can only be modified manually; however, services can be added, erased or modified dynamically. There is dynamic information that is constantly being modified: the service performance (average time to respond to requests), the number of executions, and the quality of the service (QoS). This last data assigns a value between 0 1 to all services. All new services have a quality of service value set to 1. This value decreases when the service fails (e.g. service crashes, no service found, etc.) or has a subpar performance compared to similar past executions. Security must be a major concern when developing this kind of systems. For this reason CISM@ does not implement a service discovery mechanism. However, agents can select the most appropriate service (or group of services) to accomplish a specific a task. 4. Control Agent. This agent supervises the correct functioning of the other agents in the system. 5. Security Agent. This agent analyzes the structure and syntax of all incoming and outgoing XML messages. 6. Manager Agent. The Manager Agent decides which service must be called by taking into account the QoS and users' preferences. Users can explicitly invoke a service, or can let the Manager Agent decide which service is the best to accomplish the requested task 7. Interface Agent. This kind of agent was designed to be embedded in users' applications. Interface agents communicate directly with the agents in CISM@. These agents must be simple enough to allow them to be executed on mobile devices, such as cell phones or PDAs. All high demand processes must be delegated to services. CISM@ is an open architecture that allows developers to modify the structure of the agents described before. Developers can add new agent types or extend the existing ones to conform to their projects needs. However, most of the agents' functionalities should be modeled as services, releasing them from tasks that could be performed by services.

Conclusions and Future Work

CISM@ facilitates the development of dynamic and intelligent multi-agent systems. Its model is based on a cloud computing approach where the functionalities are implemented using Web Services. The architecture proposes an alternative where agents act as controllers and coordinators. CISM@ exploits the agents' characteristics to provide a robust, flexible, modular and adaptable solution that can cover most requirements of a wide diversity of distributed systems. All functionalities, including those of the agents, are modeled as distributed services and applications.

One of the objectives of the research activity is testing the application of Cloud Computing and Cloud services to systems and platforms oriented to Ambient Intelligent environments. Therefore, the next step in this research will be the testing of the proposed architecture in a real case study; in particular, its implementation in an ehealth system to enhance assistance and health care for patients with dependencies. This system is suitable for validating the architecture because includes a large of distributed services with different natures which can be integrated to test the architecture.

As a conclusion we can say that although CISM@ is still under development, preliminary results demonstrate that it is adequate for building complex systems and exploiting composite services. However, services can be any functionality (mechanisms, algorithms, routines, etc.) designed and deployed by developers. CISM@ has laid the groundwork to boost and optimize the development of future projects and systems that combine the flexibility of a cloud computing approach with the intelligence provided by agents. CISM@ makes it easier for developers to integrate independent services and applications because they are not restricted to programming languages supported by the agent frameworks used (e.g. JADE, OAA, RETSINA). The distributed approach of CISM@ optimizes usability and performance because it can obtain lighter agents by modelling the systems' functionalities as independent services and applications outside of the agents' structure, thus these may be used in other developments.

Acknowledgments

The heading should be treated as a 3rd level heading and should not be assigned a number.


Michele Casalini
email: michele@casalini.itwww.casalini.itorcid.org

Managing Director

Casalini Libri

BIBFRAME and Linked Data practices for the stewardship of research knowledge

This article considers need for more visible, available, accessible, innovative and shared bibliographic data in the internet age and the subsequent benefits of these transformations for galleries, libraries, archives and museums. Recent and ongoing research and development activities in the following fields are explored: entity identification, reconciliation, data enrichment, MARC records enriched with URIs, conversion to RDF, creation of relationship criteria for the improved identification of entities and a knowledge base of clusters that uses the paradigms of the semantic web. These improvements are discussed in the context of the BIBFRAME (Bibliographic Framework Initiative) data model and associated projects such as SHARE-VDE. The aim of the article is to outline current and future research and development activities in collaboration with the library community concerning the dissemination and discoverability of bibliographic data and research knowledge.

BIBFRAME and Linked Data practices for the stewardship of research knowledge 1. BIBFRAME and Linked Data

The emerging BIBFRAME (Bibliographic Framework Initiative) data model for the future advancement of bibliographic formats is currently the subject of discussion and development within the galleries, libraries, archives and museums (GLAMs) community. The new framework is intended to open up the possibilities of Linked Data, providing greater visibility and discoverability of all resource types, embracing various scripts, making bibliographic information more flexible and accessible to end users across the web rather than just library, archive or museum patrons. Many organisations are beginning to experiment with this framework and develop new workflow and business models to respond to changing needs.

The aim of this paper is very practical, focusing on recent and ongoing research and development activities: entity identification, reconciliation, data enrichment, with URIs (Uniform Resource Identifier) enhanced MARC records, conversion into RDF (Resource Description Framework), creation of relationship criteria useful to increase the effectiveness of entity identification, knowledge base of clusters that uses the paradigms of the semantic web, the BIBFRAME three layer architecture portal will be addressed. The SHARE Virtual Discovery Environment in Linked Data project encompassing a group of North American institutions with a range of different systems, habits and cataloguing traditions will be described (www.share-vde.org).

The theoretical context of these recent developments is:

Experience

Casalini Libri was established in 1958 with the dual purpose of advancing the profile of Italian culture and learning across the globe as well as providing a first-class bibliographic search and supply service for academic libraries. The company has grown considerably since its foundation by Mario Casalini, becoming one of the leading suppliers of European publications and related library services. Still a family-run business, Casalini Libri's ninety-strong team remains both faithful to the traditions of the business and committed to innovation, facilitating selection, acquisition and processing workflows working with thousands of publishers and libraries.

One of the priorities of Casalini Libri (www.casalini.it) has always been the provision of quality bibliographic information. The company produces more than 40,000 original bibliographic records for Romance Language publications each year, all of which are accessible through the online ilibri database (www.ilibri.com). Casalini Libri contributes new authority records to the national authority file as well as maintaining existing records, makes subject and classification proposals through participation in the NACO and SACO programs of the Program for Cooperative Cataloguing (PCC), and became recently ISNI Registration Agency. The records are created in native MARC21 according to the RDA BIBCO Standard Record (BSR) guidelines using the in-house WeCat cataloguing module of the OLISuite ILS, developed by @Cult.

A consulting and software development company established in 2001, @Cult (www.atcult.it) delivers effective and innovative tech solutions to improve information management and knowledge sharing. Casalini Libri's IT Department and @Cult have a longstanding collaboration and have been working together for over 10 years. One of the several projects in the field of the semantic web for cultural heritage institutions which @Cult has been involved in is ALIADA.

The ALIADA (Automatic publication under Linked Data Paradigm of Library Data) project was cofinanced by the European Union's Research and Innovation funding programme and ran from 2013 to 2015. It involved five partners from Italy, Spain and Hungary. The project originally applied the Linked Data paradigm using FRBRoo based ontologies. The project was developed with the aim of supporting the entire process, from conversion to the publication and linking of data. The results of the project are available from www.aliada-project.eu.

BIBFRAME and Linked Data practices: the SHARE Virtual Discovery Environment in Linked Data project (SHARE-VDE)

Following the success of ALIADA and after input received from the library community, in 2014 Casalini Libri embarked upon a strategy for the progressive implementation of the BIBFRAME data model in close collaboration with @Cult. After a year of initial study and a feasibility analysis Casalini Libri presented the first results from the joint venture and a plan for action at the BIBFRAME LC Forum in Boston January 2016. In the subsequent months, following discussions with the library community, a research and development project, so far involving sixteen North-American institutions, was established: SHARE Virtual Discovery Environment in Linked Data (www.share-vde.org).

Project highlights

SHARE-VDE is a library community driven research and development project to establish entity identification, reconciliation and conversion processes as well as a prototype of a virtual discovery environment with a BIBFRAME three layered architecture (Person/Work, Instance, Item). Furthermore, the project will create a database of relationships that is open to the community and a common knowledge base of clusters that uses the paradigms of the semantic web but also allows the libraries to continue to handle their data as independently as possible.

In terms of outcomes for partner libraries the project aims to create an environment that is useful for both library patrons, empowering them with advanced discovery interfaces, and librarians, incrementally providing them with cataloguing functions in native semantic web standards, integrating processes with the local systems, and implementing tools in a collaborative environment. SHARE-VDE also hopes to help to reveal a richness within the data of existing collections, often hidden or unexpressed in a traditional catalogue. This will be carried out through discussion, experimentation and configuration of the options for the future data creation, enhancement and sharing of all type of resources with the library community. Emphasis is placed on the use of short phases, yielding tangible results, on which institutions can base future decisions and further steps.

Among the guiding principles of the initiative are the need for independence from the different local systems (ILS), habits and cataloguing traditions already in place; for the components to be available individually in as flexible as possible configuration as expected by the needs of various library -but also archive and museum -community groups. Scalability of the tools is highly important and the project aims to test these new tools using over 100 million traditional bibliographic and authority records. Alongside the ability to address issues and problems related to new information management processes, and taking into account the complexity of the long transition time that will see the coexistence of native MARC and RDF data.

The project is divided in three phases and is directed by Tiziana Possemato, Chief Information Officer of Casalini Libri and Director of @Cult.

Phase 1 (October 2016 -January 2017): two sets of data for each participating library were anticipated, consisting of the titles with imprint year 1985 and 2015 within each library's system. The advantage of having two data sets was the ability to test the processes in a complex environment with a significant number of cases that come from different stages of the library catalogue, including various type of resources and scripts. This phase resulted in a total of 2,308,204 bibliographical records with 3,601,327 authority records being reconciled, enriched, converted into BIBFRAME 2.0 and published on the SHARE-VDE portal.

Phase 2 (March -December 2017): the complete library catalogue of each participating institution are converted into BIBFRAME 2.0 and returned to each library following an overall reconciliation process and applying enhanced tools based on the feedback from phase 1. Over 100 million records and associated datasets are on course to be processed, undergoing the multiple coherent steps to conversion.

Phase 3 (2018 onward): this phase will be implemented in production based on input and use cases collected from the interested institutions. In the following sections the four major components of the project are described.

Entity identification, reconciliation and data enrichment

Entity identification is a highly relevant component of the pathways for researching and locating resources. This is why it has traditionally been considered an integral aspect of cataloging. However, the use of attributes to uniquely identify a person or a work has not previously been widely used.

With the presence online of different catalogues and authority files available in various formats, where possible in open mode, the concepts of authority control and of catalogue unification have evolved into the grouping of an entity's identifying attributes from different sources. The process is best known as reconciliation and consists of creating a cluster of data that all refer to the same entity. This entity may be known by different names deriving from cultural differences, disparity in cataloguing rules, linguistic variations, and simple typographical errors. Reconciliation accepts this variety, turning it to the advantage of both the cataloguer and the end user.

The enrichment of records, derived through connections to authority files (centralised, distributed or local), other external sources and from clustering data from specific projects, has extraordinary potential to enhance their function. It enables end users to expand their research on the entity increasing their chance of finding new information and resources, while at the same time allowing libraries to consult other authoritative general or specialised sources.

These conditions are also prerequisite for a revolution in the concept of cataloguing and how a catalogue is presented. The change from the record in its entirety having meaning in its rigidity, to the entities as real things in the world, recognising how flexibility and diversity can enrich information.

Figure 4: The new revolution: from record to entity Data reconciliation and enrichment is obtained by means of complex logic and algorithms (data comparison, results filtering, validation etc.), which may be carried out using either automated systems or manual processes, included (where the ILS permits it) in the cataloguing workflow. The relationship between the reconciliation and validation of the results can differ profoundly between the automated and manual processes as the automated processes assure a high-level of reconciliation and clustering with a low-level of validation of results versus the manual processes with a low-level of reconciliation and clustering and a high-level of validation of results. The best outcome, based on the weighing of parameters during the automated process, can often be a compromise of the two. In SHARE-VDE Authify is the tool for automated reconciliation. It is a RESTFul module that offers several full-text search services among names and work clusters, and relator terms detection services. The manual tool is a the URI Management System embedded into Casalini Libri's OLISuite WeCat cataloging suite.

In addition to these there are two more components that are part of the overall system:

I.
The database of relationships created from the analysis of bibliographic and authority records with the aim to make evident the relationships that are contained within these records (between author and publishers, author and subjects, publisher and areas of interest, authors and collaborators, titles and ISBN etc.) with the final goal of these procedures being to provide a more effective identification of the entities of interest.

II.

The knowledge base of clusters with GET services to retrieve the cluster data and PUT services to create new clusters. A common knowledge base as a web accessible source with reconciled entities identified with RWO URIs can also be made accessible via API/WS or SPARQL endpoint in RDF format.

Enhanced MARC records with URIs

In the recent months important steps were achieved by the PCC Program for Cooperative Cataloging (PCC) Task Groups on URIs in MARC & BIBFRAME, in conjunction with the MARC Advisory Commitee. These decisions embraced the redefinition of subfield $0 for recording URIs which represent objects in RDF triple statements, and of subfield 4 to be used for recording URIs which represent predicats in RDF triple statements.

One aspect always to be taken into consideration in the application of data models is the conversion of data into alternative structures without the loss of content. To ensure an effective transition from the MARC record to BIBFRAME, the implementation plan must ensure that MARC data elements are enriched through the addition of the necessary local and global identifiers.

Once the automatic and manual processes required for this procedure are established, MARC can be converted into Linked Data by any entity.

For an original cataloguing data producer and provider, such as Casalini Libri, it's important to embed the ability to parameterise workflows and foresee profiling options to handle, for example the personalisation of URI sources according to the preferences of each individual institution.

As the transition from MARC to the RDF environment will occur over a substantial period, heterogeneous systems will coexist for long time, probably also within the same institution.

Conversion of authority and bibliographical data in BIBFRAME

In SHARE-VDE Lodify is the tool in command of the conversion to RDF. It contains an asynchronous pipeline where the process is split into pieces (processors), each of these responsible for a small part of the overall task. Each processor can act as a splitter or aggregator and can achieve content manipulation of the incoming message.

Following the ALIADA methodology, Lodify converts each incoming record by means of conversion templates. Each template associates a MARC record from the incoming data-stream with a set of (conversion) rules associated with one or more ontologies.

Publication of a BIBFRAME three layered platform prototype

Current catalogue data predominantly contains descriptions of Manifestations (in FRBR) / Instances (in BIBFRAME). The objective is now to respond to the need to re-design this data model to include a system that derives data from existing records to produce a new, higher Person / Work layer giving significant advantages for the end user.

In order to achieve this aim the data, after being processed through the steps described above, are presented on a portal equipped with navigational tools based on the BIBFRAME data model characterized by three different layers:

 Person / Works: this level is enriched by data from sources external to the library catalogues for the purpose of extending the research potential.

 Instances (or Publications): the Instances level is associated with Publications and connected to the overlying layer through relationships with the Works present.

 Item: each Instance (Publication) is linked to information about the data set and the availability of the copy present in the local OPAC of each library.

In order to move progressively toward a record-less approach the platform also addresses the Instance reconciliation aspect. On the Item level, API or web services can be implemented to communicate with the local OPAC. In addition, diversified user interfaces can be applied for different user community needs.

The first version of the SHARE platform was developed by @Cult whilst working on a smaller scale project that went into production in spring 2016. It involved seven Italian university libraries that used and continue to use different local systems: some based on MARC21, others on UNIMARC, also applying different cataloguing codes. The platform can be viewed at http://catalogo.sharecat.unina.it/sharecat/clusters?l=en.

A further application is ilibri-up, an enhancement of Casalini Libri's existing ilibri bibliographic database, which will also serve the main link for the ISNI Registration Agency activities of Casalini Libri.

The SHARE-VDE platform is accessible at http://www.share-vde.org.

The following series of figures depicts an overview of the SHARE-VDE processes as well as a series of examples.

Further connected topics

A number of further connected topics and enhancements are addressed throughout the project, among them:

 User interface:  For both Person/Work and Instance layers: search-box for relationship presentation (e.g. author to subject, author to publisher);  First edition identification for the chronological positioning of Entities;  "Work of" or "Work concerning" a Person.

 Additions of classes and properties originating from ontologies other than BIBFRAME as needed.

 Subject URI enrichment; content, media, carrier enrichment.

 Analysis for the creation of relationships among subject terms and strings in different languages.

 Provenance declarations that can become the fourth element added to every triple.

 Update management and URI Registry.

In order to address the many involved aspects particularly useful have been the brainstorming opportunities among Linked Data for Production (LD4P), IMLS Shareable Authorities Forum, Linked Open Data in Libraries Archives and Museums (LODLAM), Program for Cooperative Cataloguing (PCC) and European RDA Interest Group (EURIG) contacts and meetings.

Conclusions

The scope of the briefly described project has been chosen so that decisions for subsequent steps for a production scenario for the GLAM communities can be based on concrete evidence from a conspicuous data sets. This can then contribute to building a realistic model of the activities, the problems to be addressed as well as the potential advantages of moving toward the Linked Data environment. Functionality also dominated the design of the project: providing various environments and user interfaces for data creation, enrichment and supply workflows to the diverse group of participating librarians, professionals, scholars, researchers and students encompassing a wide range of needs.

Promoting a culture of openness towards knowledge has multiple advantages for all of the links in the information chain. The exploitation and diffusion of library, archive and museum data to a wider audience, enriching the World Wide Web with valuable information that until now has remained hidden in archives, collections and catalogues, allows libraries and museums to benefit from the opportunity to provide more comprehensive tools, while end users are able to access a wealth of information.

All research, but perhaps the Humanities in particular, needs now more than ever to be visible, available, accessible and innovative. By using the increased discoverability offered by the projects discussed, perceptions of research fields not foremost in the mind of the public at large can be changed and their importance for society acknowledged, also reducing the risk of niche subject areas being marginalised. For this to occur, increased access to knowledge is vital and key to achieving this is collaboration among all stakeholders. Linked Data practices give vital support to the stewardship of research and introduce an invaluable opportunity that can contribute to taking forward cultural heritage for future generations.

Figure 1 :

 1 Figure 1: Brief theoretical context

Figure 2 :

 2 Figure 2: BIBFRAME 2.0 data model

Figure 3 :

 3 Figure 3: Casalini Libri's BIBFRAME Conversion, Distribution & Publication Options

Figure 5

 5 Figure 5 and 6: Authify tool in SHARE-VDE to obtain more comprehensive and precise URI retrieval and automated process of cluster creation for Person-and Work-type entities.

Figure 7 :

 7 Figure 7: Using the URI Management System in the WeCat cataloguing module of the OLISuite ILS example of manual entity enrichment carried out in the cataloguing workflow (the availability of API and web services allows the use of external sources -in this example, NAF, ISNI and VIAF).

Figure 8 :

 8 Figure 8: Lodify tool in SHARE-VDE for the conversion into RDF.

Figure 9 :Figure 11 :Figure 13 :

 91113 Figure 9: SHARE-VDE overall processes

  The institutions participating in phase 1, 2, or both phases of the project are the following: Stanford University, University California Berkeley, Yale University, Library of Congress, University of Chicago, University of Michigan Ann Arbor, Harvard University, Massachusetts Institute of Technology, Duke University, Cornell University, Columbia University, University of Pennsylvania, Pennsylvania State University, Texas A&M University, University of Alberta, University of Toronto.