Jean-Gabriel Ganascia

Catherine Tessier

Thomas M Powers

On the autonomy and threat of "killer robots"

Introduction

In the past, renowned scientists such as Albert Einstein and Bertrand Russell publicly engaged, with courage and determination, the existential threat of nuclear weapons. In more recent times, scientists, industrialists, and business leaders have called on states to institute a ban on what are -in the popular imagination -"killer robots." In technical terms, they are objecting to LAWS (Lethal Autonomous Weapons Systems), and their posture seems similar to their earlier, courageous counterparts. During the 2015 International Joint Conference on Artificial Intelligence (IJCAI) -which is the premier international conference of artificial intelligence -some researchers in the field of AI announced an open letter warning of a new AI arms race and proposing a ban on offensive lethal autonomous systems. To date, this letter has been signed by more than 3,700 researchers and by more than 20,000 others, including (of note) Elon Musk, Noam Chomsky, Steve Wozniak, and Stephen Hawking.

In the summer of 2017, at the most recent IJCAI held in Melbourne, Australia, another open letter was presented, signed by the heads of many companies in the fields of robotics and information technologies, among whom Elon Musk was very active. This second letter urged the United Nations to resume its work towards a ban on autonomous weapons, which had been suspended for budgetary reasons.

It is no doubt incumbent on every enlightened person, and in particular on every scientist, to do everything possible to ensure that the industrialized states give up the idea of embarking on yet another mad arms race, the outcome of which might escape human control. This seems obvious, especially since, according to the authors of these two open letters, we would be at the dawn of a third revolution in the art of war, after gunpowder and the atomic bomb.

If these positions appear praiseworthy at first, should we not also wonder about the actual threats of these lethal autonomous weapon systems? To remain generous and sensitive to great humanitarian causes, should we not also remain rational and maintain our critical sensibilities? Indeed, even though considerable ethical problems arise in the evolution of armaments -from landmines to drones, and recently to the massive exploitation of digitized information and electronic warfare -it appears on reflection that this third revolution in the art of war is very obscure. Where the first two revolutions delivered considerable increases in firepower, we find here an evolution of a very different order.

Moreover, the so-called "killer robots" that have been the targets of three years of numerous press articles, open letters and debates, seem to be condemned by sensational and anxiety-laced arguments, mostly to the exclusion of scientific and technical ones. The term "killer robot" suggests a robot that would be driven by the intention of killing and would even be conscious of that intention, which at this stage in the science does not make sense to attribute to a machine -even one that has been designed for destroying, neutralizing or killing. For instance, one does not speak of a "killer missile," when it happens that a missile kills someone. "Killer robot" is a term that is deployed for rhetorical effect, that works to hinder ethical discussion, and that aims at manipulating the general public. Do the conclusions of these arguments also hold against "killing robots"? Is there an unavoidable technological path from designing "killing robots" to deploying "killer robots"?

To get a better understanding of these questions, we aim here to put forward a detailed analysis of the 2015 open letter, which was one of the first public manifestations of the desire to ban LAWS. Our reservations concerning the declarations that this letter contains should help to open the scientific and philosophical debates on the controversial issues that lie at the heart of the matter.

The Argument for a Ban

The 2015 open letter was revealed to journalists and, by extension, to a broad audience during the prestigious IJCAI in Buenos-Aires, Argentina. In its first sentence, the letter warned that "[a]utonomous weapons select and engage targets without human intervention," and concluded after four short paragraphs by calling for a ban on offensive forms of such weapons. This public announcement had been preceded by an invitation for signatories within the AI scientific community and beyond, including a wider community of researchers, technologists, and business leaders. Many of the most prominent AI and robotics researchers signed it, and outside the AI community many prominent people brought their support to this text. Initially, the renown and humanitarian spirit of the co-signers may have inclined many people to subscribe to their cause. Indeed, the possibility of autonomous weapons that select their targets and engage lethal actions without human intervention appears really terrifying. However, after a careful reading of the first open letter, and in consideration of the subsequent public statements on the same topics, e.g., the IJCAI 2017 (second) open letter, and video 1 that circulated widely on the web towards the end of 2017, we think a closer analysis of the deployed arguments clearly shows that the letter raises many more questions than it solves. Despite the fame and the scientific renown of the signatories, many statements in the letter seem to be questionable from a scientific point of view. In addition, the text encompasses declarations that are highly disputable and that will certainly be belied, very soon, by upcoming technological developments. These are the reasons why, as scientists and experts in the field, it seems incumbent upon us to scrutinize the claims that these public announcements contain and to re-open the debate. We are not disparaging the humanitarian aims of the authors of the letter; we do however want to look more closely at the science and the ethics of this issue. Even though we share the same feeling of unease that has likely motivated the authors and the signatories of these open letters, we want to bring into focus where, we believe, the scientific case is lacking for the normative conclusion they draw.

For ease of reference, the content of the 2015 Open Letter has been appended to this article, with numbered lines added to facilitate comparison between our text and theirs.

The first paragraph (l. 10-17) describes recent advances in artificial intelligence that will usher in a new generation of weapons that qualify as autonomous because they "select and engage targets without human intervention." These weapons will possibly be deployed "within years, not decades" and will constitute "the third revolution in warfare, after gunpowder and nuclear arms." The next paragraph (l. 18-33) explains why a military artificial intelligence arms race would not be beneficial for humanity. The two main arguments are, first, that "if any major military power pushes ahead with AI weapon development, a global arms race is virtually inevitable" and second, as a consequence, "autonomous weapons will become the Kalashnikovs of tomorrow," i.e. they will become ubiquitous because they will be cheap to produce and distribution will flow easily from states to non-state actors. In addition, this paragraph warns that autonomous weapons are "ideal" for dirty wars, i.e. "assassinations, destabilizing nations, subduing populations and selectively killing a particular ethnic group." The third paragraph (l. 34-40) draws a parallel between autonomous weapons and biological or chemical weapons, the development of which most scientists have rightly shunned. AI researchers, it is implied, would "tarnish their field" by developing AI weapons. Finally, the last paragraph (l. 41-44) summarizes the content of the letter and then calls for a ban on offensive autonomous weapons.

Our perplexity comes from these four aspects of the general argument, as developed in the letter:

1. The notion of "autonomous weapon" that motivates the letter is obscure; its novelty and what distinguishes it from AI weapons in general are sources of confusion. At least this much is certain: not all AI weapons are autonomous, according to the definition given by the authors (selecting and engaging targets without human intervention.) Contrary to what is claimed, the technical feasibility of autonomous weapons deployment in the near future is far from obvious. 2. Despite the dramatic illustrations given in the letter, and repeated in the video to which we referred above, the specific noxiousness of autonomous weapons that makes them "ideal" for dirty military actions and that differentiates them from current weapons is not obvious from a technical point of view. 3. The analogy between the current attitude of AI scientists faced with the development of autonomous weapons and the past attitude of scientists faced with the development of chemical and biological weapons is far from clear. Besides, the parallel between the supposed outbreak of autonomous weapons in contemporary military theaters and the advent of gunpowder or nuclear bombs in warfare is highly debatable. 4. Lastly, the ban on offensive autonomous weapons is not new and is already being discussed by military leaders themselves, which makes this declaration somewhat irrelevant.

The remainder of this article is dedicated to a deeper analysis of the four points above.

Autonomous Weapons

What exactly is the notion of "autonomous weapon" to which the letter refers? Autonomy is the capability for a machine to function independently of another agent (human, other machine) exhibiting non-trivial behaviors in complex, dynamic, unpredictable environments [1]. The autonomy of a weapon system would involve sensors to assist in automated decisions and machine actions that are calculated without human intervention. Understood in this way, autonomous weapons have already existed for some time, as exemplified by a laser-guided missile that "hangs" a target.

The current drones that are operated and controlled manually at more than 3000 km from their objectives use such autonomous missiles. If this were the meaning of "autonomous weapons" in this letter, the notion would correspond only to a continuous progression in military techniques. In other words, this would just be an augmentation in the distance between the "soldier" (or, more precisely the operator) and its target. In this respect, among a bow-and-arrow, a musket, a gun, a canon, a bomber and a drone, there is just a difference in the order of magnitude of the arms' ranges. However, the text of the open letter does not say this, but rather claims that (l. 10) [a]utonomous weapons select and engage targets without human intervention. The question then is not about the range of action but about the "logical" nature of the weapon: until now, and for centuries, a human soldier aimed at the target before firing, while in the future, with autonomous weapons, the target will be abstractly specified in advance. In other words, the mode of designating the target changes. While up to now, the objective, i.e., the target, was primarily an index on which the human aimed, in the near future it will just become an abstract symbol designated by a predefined rule. Since no human is involved in triggering the lethal action, this evolution of warfare seems terrifying, which would justify the concerns of the open letter.

Let us note that the concept of "autonomy" is problematic, firstly because various stakeholders (among them, scientists) give the term multiple meanings [START_REF] Boulanin | Mapping the development of autonomy in weapon systems -Stockholm International Peace Research Institute[END_REF] [3]. An "autonomous weapon" can thus designate a machine that reacts automatically to certain predefined signals, that optimizes its trajectory to neutralize a target for which it has automatically recognized a predefined signature, or that automatically searches for a predefined target in a given area. Rather than speaking of "autonomous weapons," it seems more relevant to study which functions are or could be automated, which is to say, delegated to computer programs. Further, we should want to understand the limitations of this delegation, in the context of a sharing of authority (or control) with a human operator, which sharing may vary during the mission.

Guidance and navigation functions have been automated for a long time (e.g. automatic piloting) and have not raised significant questions. These are non-critical operational functions. But automatic identification and targeting are more sensitive functions. Existing weapons have target recognition capabilities based on predefined models (or signatures): the recognition software matches the signals received by the sensors (radar signals, images, etc.) with its signature database. This recognition generally concerns large objects that are "easy" to recognize (radars, airbases, tanks, missile batteries). But the software is unable to assess the situation around these objects -for example, the presence of civilians. Targeting is carried out under human supervision, before and/or during the course of the mission.

Ineluctability

The authors seem to suggest that this evolution is ineluctable because, if specification of abstract criteria and construction of the implementing technology is cheaper and faster than recruiting and training soldiers, and assuming that modern armies have the financial and technical wherewithal to make these weapons, then autonomous weapons will eventually predominate. This complicated point deserves some more in-depth analysis, since the definition of the criteria to which the open letter refers appears sometimes very problematic, despite the progress of AI and machine learning techniques. Many problems remain to be solved. For instance, how will the technology differentiate enemies from friends in asymmetric wars, where the soldiers don't wear uniforms? More generally, when humans are not able, on the basis of a given set of information, to discriminate cases that meet criteria from cases that don't, how will machines do better? If humans cannot discern, from photos, which are the child soldiers and which are children playing war, it is illusory to hope to build a machine that automatically learns these criteria, on the basis of the same set of information. Will algorithms be able to recognize a particular individual from their facial features, a foe from their military uniform, a person carrying a gun, a member of a particular group, a citizen of a particular country whose passport will be read from a remote device? It will be impossible to build a training set.

In recognition of these remaining problems, it seems that the supposed ineluctability of the evolution that would spring from the AI state of the art is debatable, and certainly not "feasible within a few years" as the letter claims. It would have been more helpful had the authors of the letter elaborated on what precisely will be feasible in the near future, especially as far as automated situation assessment is concerned. The assertion that full-blown autonomous weapons are right around the corner would then have been placed in context.

On the Formal Specifications of Autonomy

Current discussions and controversies focus on the fact that an autonomous weapon would have the ability to recognize complex targets in situations and environments that are themselves complex, and would be able to engage (better than can humans) such targets on the basis of this recognition. Such capabilities would suppose the weapon system:

• to have a formal (i.e. mathematical) description of the possible states of the environment, of the elements of interest in this environment and of the actions to be performed, even though there is no "standard situation" or environment • to recognize a given state or a given element of interest from sensor data • to assess whether the actions that are computed respect the principles of humanity (avoid unnecessary harms), discrimination (distinguish military objectives from populations and civilian goods), and proportionality (adequacy between the means implemented and the intended effect) of the International Humanitarian Law (IHL).

Issues of a philosophical and technical nature are related to the ability of the system to automatically "understand" a situation, and in particular to automatically "understand" the intentions of potential targets. Today, weapon system actions are undertaken with human supervision, following a process of assessment of the situation, which seems difficult to formulate mathematically. Indeed, the very notion of agency, when humans and non-human systems act in concert, is quite complicated and also fraught with legal peril.

Beyond the philosophical and technical aspects, another issue is whether it is ethically acceptable that the decision to kill a human being, who is identified as a target by a machine, can be delegated to this machine. More specifically, with respect to the algorithms of the machine, one must wonder how and by whom the characterization, model and identification of the objects of interest would be set, as well as the selection of some pieces of information (to the exclusion of some others) to compute the decision. Moreover, one must wonder who would specify these algorithms and how it would be proven that they comply with international conventions and rules of engagement. And as we indicated above, the accountability issue is central: who should be prosecuted in case of violation of conventions, or misuse? It is our contention that these difficult formal issues will delay (perhaps indefinitely) the advent of the sort of autonomous weapons that the authors so fear.

Finally, it is worth noting that the definition of autonomous weapons (Autonomous weapons select and engage targets without human intervention (l. 10)) comes from the 2012 U.S. Department of Defense Directive Number 3000.09 (November 21, 2012. Subject: Autonomy in Weapon Systems). Nevertheless, the authors of the letter have truncated it. As a matter of fact, the complete definition given by the DoD directive is the following: Autonomous weapon system: a weapon system that, once activated, can select and engage targets without further intervention by a human operator. This includes human-supervised autonomous weapon systems that are designed to allow human operators to override operation of the weapon system, but can select and engage targets without further human input after activation.

From the DoD directive one learns in particular that (3) "Autonomous weapon systems may be used to apply non-lethal, non-kinetic force, such as some forms of electronic attack, against materiel targets" in accordance with DoD Directive 3000.3. Therefore, we should bear in mind that a weapon (in general) should be distinguished from a lethal weapon. Indeed, a weapon system is not necessarily a system that includes lethal devices.

Hence, the proffered, alarming example of what autonomous weapons technology could bring -"armed quadcopters that can search for and eliminate people meeting certain pre-defined criteria" (l. 11-12) -seems more fitting for the tabloid press. For this example to be taken seriously, some of those targeting criteria should be made explicit, and current and future technology should be examined as to whether a machine would be able to assign instances to criteria, with no uncertainty, or with less uncertainty than a human assessment. For example, the criterion "target is moving" -for which no AI or autonomy is required -is very different from the criterion "target looks like this sketch and attempts to hide."

Harmfulness

The second paragraph (l. 18-33) is mainly focused on the condemnation of automated weapons.

The Ethics of Robot Soldiers

From the beginning, this paragraph seems intended to measure the costs and benefits of autonomous weapons, but it proceeds too quickly by dismissing debates about the possible augmentation or diminution of casualties with AI-based weapons. While the arguments for augmentation rely upon the possible multiplication of armed conflicts, the arguments for diminution seem to be based on the position of the roboticist Ronald Arkin [START_REF] Arkin | Governing Lethal Behavior in Autonomous Robots[END_REF]. According to Arkin, robot soldiers would be more ethical than human soldiers, because autonomous machines would be able to keep their "blood cold" in any circumstance and to obey the laws of the conduct of a just war. Note that this argument is suspect, because the relevant part of just war laws -the conditions for just conduct or jus in bellum -are based on two further principles. As we indicated above, the principle of discrimination, according to which soldiers have to be distinguished from civilians, and the principle of proportionality, which limits a response to be proportional to the attack, are both crucial to building an ethical robot soldier. Neither discrimination nor proportionality can be easily formalized, so it is unclear how robot soldiers could obey the laws of just war. The problem is that, as mentioned in the previous section, there is no obvious way to extract concrete objective criteria from these two abstract concepts. However, interestingly, the open letter never mentions this formal problem, even though it could help to reinforce its position against autonomous weapons.

Ideal Weapons for Dirty Tasks

The main argument concerning the harmfulness of autonomous weapons is that they "are ideal for tasks such as assassinations, destabilizing nations, subduing populations and selectively killing a particular ethnic group." The different harms belonging to this catalogue appear to be highly heterogeneous. What is common to these different goals? Further, the adjective "ideal" is particularly obscure. Does it mean that these weapons are perfectly appropriate for the achievement of those dirty tasks? If that is the case, it would have helped to give more details and to show how autonomous weapons would facilitate the work of assailants. Such an elaboration would have been important because, at first glance, there is no evidence that autonomous weapons will be more precise than classical weapons (e.g. drones) for assassination or selective killing of a particular ethnic group. Indeed, it is difficult to imagine how autonomous machines could select, more efficiently than other weapons, the individuals that are to be killed, or discern expeditiously members of human groups, depending on their race, origin or religion. Finally, the underlying premise of the "harmfulness" argument is worth questioning, for it is not clear that those conducting "dirty wars" care much about precision or selectivity. Indeed, this "not caring" may be a central trait of the "dirtiness" of such aggression.

Necessary Distinctions

Underlying the discussion of these loosely-related "dirty" tasks and a possible arms race, there is a confusion between three putative properties of autonomous weapons that, taken one by one, are worth discussing: firepower, precision and diffusion. Despite the reference to gunpowder and nuclear weapons (l. 16-17, 24, 40), there is no direct relation between autonomy of arms and their firepower. Further, it is not any more certain that autonomous weapons would reach their targets more precisely than classical weapons. The series of "drone papers" 2 shows how difficult it is to systematize human targets selection and to automatically gather exact information on individuals by screening big data. Lastly, the argument about the diffusion of autonomous weapons is in contradiction with the supposed specific role of major military powers in autonomous weapon development. More precisely, the problem appears when we consider the following claims:

1. If any major military power pushes ahead with AI weapon development, a global arms race is virtually inevitable (l. 21-23), (which we consider to be probable) 2. autonomous weapons will become the Kalashnikovs of tomorrow (l. 24), (which is also possible) However, even if claims 1 and 2 above are plausible separately, they seem jointly implausible. (By comparison, the development of nuclear weapons did start an arms race, but also kept nuclear armaments out of the hands of all but the "nuclear club" of nations) There may even be an antinomy between 1 and 2, because if only major military powers would be able to promote scientific programs to develop autonomous weapons, then it is likely that these scientific programs would be too costly to develop for industries, without rich state support, or for poor countries or non-state actors, which means that these arms couldn't so quickly become sufficiently cheap that they would spread throughout all humankind. Some weapons might be more easily replicated, once information technologies have been developed, and military powers could act as pioneers in that respect. However, nowadays, it appears that military industries are not guiding technical development in information technologies, as was the case in the 20 th century (at least until the end of the seventies), but that more often the opposite is the case: information technology industries (and dual-purpose technologies) are ahead of the military technologies. Undoubtedly, information technology industries would become prominent in developing autonomous weapons technologies if there were a mass market for autonomous weapons, as the authors of this open letter assume. Lastly, if these technologies were potentially so cheap that they could be spread widely, there would be a strong incentive for the major military powers to keep "a step ahead" to ensure the security of their respective populations.

The paragraph ends with a rather strange sentence (l. 32-33): "There are many ways in which AI can make battlefields safer for humans, especially civilians, without creating new tools for killing people." This suggests that AI would benefit defense whereas autonomous weapons would not. Nevertheless, what has been argued previously against autonomous weapons can fit all other AI applications in defense in the same way. Moreover, and to add to the confusion in this claim, the terms autonomous weapon (l. 10, 15, 18, 24, 29, 43), AI weapon (l. 22, 35) and AI arms (l. 21, 31, 42) seem for the authors to be interchangeable or synonymous phrases. Yet equipping a weapon, whether lethal or not, with some AI (e.g. a path planning function) does not necessarily make it autonomous and conversely some forms of autonomy (e.g. an autopilot) may hinge on automation without involving any AI.

Analogies with other Weapons

A third central claim in the general argument concerns military analogies with other weapons: nuclear weapons on the one hand and biological and chemical weapons on the other. All of these parallels are troublesome.

Third Revolution in Warfare

It is announced (l. 15-17) that the development of autonomous weapons would correspond to a third revolution in warfare, after gunpowder and nuclear weapons. Later, the analogy with nuclear weapons is repeated twice (l. 24 and l. 40), in order either to draw connections or to underline differences. Based on our observations above, it does not seem that autonomous weapons will lead to an augmentation in firepower, but instead, to an increase in the distance between the soldier and his/her target. If there is something innovative in autonomous weaponry, it is in range rather than power. Therefore, it would have been better to compare autonomous weapons with the bow-and-arrow, the musket, or the bomber drone, instead of with weapons for which incidence range is totally heterogeneous.

Parallel with chemical and biological weapons

The third paragraph draws a parallel between autonomous weapons and weapons that have been considered morally repugnant, such as the chemical and biological weapons that scientists don't develop anymore, because they "have no interest in building" them, and they "do not want others to tarnish their field by doing so" (l. 34-36).

The comparison is questionable. Indeed, historically, it is mostly German and French chemists who developed many chemical weapons (mustard gas, phosgene, etc.) during the Great War. Similarly, Zyklon B had been conceived by Walter de Heerdt, a student of Fritz Haber, recipient of Nobel Prize in Chemistry, as a pesticide. The ban on chemical and biological weapons did not spring from scientists, but from the collective consciousness, after the First World War, of the horrors of their use.

In a somehow different register, the scientific community didn't oppose, as a whole, the development and deployment of nuclear weapons. The presence of a large number of great physicists in military nuclear research centers attests to this fact.

In terms of the parallel, it is far from clear that AI will lead to autonomous weapons, and far from clear that autonomous weapons will be widely viewed as morally abhorrent, compared to the alternatives.

The Ban Claim

A Ban on Offensive Autonomous Weapons

The final paragraph proposes a "ban on offensive autonomous weapons beyond meaningful human control" (l. 43-44). Nonetheless, the authors should know that many discussions have already taken place, that scientists have barely participated in these discussions, and that in the United States, in 2012, the Defense Department already decided on a moratorium on the development and the use of autonomous and semiautonomous weapons for 10 years (see above reference to the DoD Directive 3000.09).

For several years, the United Nations has also been concerned about this issue. It is therefore difficult to understand the exact position of the scientific authors of the letter, especially if it does not invoke the debates that have already taken place, and to the extent that it relies on some not-altogether-germane considerations-precision, ubiquity, illicit use, firepower, etc. -such as we have explained above.

In short, the conclusion of a ban does not seem to be justified by the general argument of the letter (given the problems we have noted), nor by the novelty of the position they are staking out. There is a ban, and states are not racing ahead to deploy offensive, lethal, autonomous weapons systems. But might we be missing something? Might the authors foresee a deeper reason for scientists and technologists to eliminate the very possibility of an unlikely but terrifying threat?

Such would be the conclusion of an argument from the "precautionary principle," which could be the motivating principle of the ban. The precautionary principle is often invoked in environmental ethics, especially in assessing geo-engineering to combat climate change. The idea is that, while new technologies promise benefits, the threat of them going astray is so cataclysmic in terms of their costs that we must act to eliminate the threat, even when the likelihood of cataclysm is very small. The imagined threat here would be the continued development of autonomous weapon systems leading to a military AI arms race, or the mass proliferation of AI weapons in the hands of unscrupulous non-state actors, as the authors of the open letter envision.

Wallach and Allen discussed a similar argument against AI in their 2009 book Moral Machines [START_REF] Wallach | Moral Machines: Teaching Robots Right from Wrong[END_REF]:

The idea that humans should err on the side of caution is not particularly helpful in addressing speculative futuristic dangers. This idea is often formulated as the "precautionary principle" that if the consequences of an action are unknown but are judged to have some potential for major or irreversible negative consequences, then it is better to avoid that action. The difficulty with the precautionary principle lies in establishing criteria for when it should be invoked. Few people would want to sacrifice the advances in computer technology of the past fifty years because of 1950s fears of a robot takeover.

In answer to the "precautionary" challenge to autonomous weapons, it seems that Wallach and Allen provide the right balance between ethical concern and scientific responsibility:

The social issues we have raised highlight concerns that will arise in the development of AI, but it would be hard to argue that any of these concerns leads to the conclusion that humans should stop building AI systems that make decisions or display autonomy.... We see no grounds for arresting research solely on the basis of the issues presently being raised by social critics or futurists.

Scientific Authors

Let us end by going to the beginning-with a consideration of the title (l.8-9): "Autonomous Weapons: An Open Letter from AI & Robotics Researchers."

Who exactly are the AI & Robotics Researchers who wrote the open letter? As a matter of fact, nothing in their presentation allows those who wrote the letter to be distinguished from those who have signed it. The question is all the more important, as some tensions within the arguments of the text suggest that some negotiations took place. In any case the open letter cannot appear as coming from all AI and robotics researchers. Some members of this community, both in Europe and in the United States -not to mention the authors of this present article -have already disagreed with the content of the open letter.

To conclude, scientists and members of the artificial intelligence community may not wish to adhere to the position expressed in the open letter, not because they are interested in developing autonomous weapons, or are not "sufficiently humanitarian," but because the arguments conveyed in the letter are not sufficiently grounded in science. We think it is our duty to publicly express our disagreement because when scientists communicate in the public sphere, not as individuals, but as a scientific community as a whole, they must be sure that the state of the art of their scientific knowledge fully warrants their message. Otherwise, such public pronouncements are nothing more than expressions of one opinion among others, and may lead to more misinformation than comprehension-they may generate "more heat than light."

It is also worth sounding another cautionary note here. When scientists decide to take the floor in the public arena, they ought to ensure that their scientific knowledge fully justifies their declarations. In these times which some commentators have declared as a "post-truth era," the rigor of scientists' arguments is more important than ever in order to fight fake-news. This can only be ascertained after they engage in debate in their respective scientific communities, especially when some of their colleagues are not in agreement with them. Otherwise, without such open dialogue -discussions which are crucial in scientific communities to establish claims of knowledge -the public may come to doubt future declarations of scientists on ethical matters, especially if they concern technological threats. Any scientific pronouncement, whether meant for an expert community or addressed to the public, ought to take utmost care to preserve scientific credibility. weapons, they require no costly or hard-to-obtain raw materials, so they will become 25 ubiquitous and cheap for all significant military powers to mass-produce. It will only be 26 a matter of time until they appear on the black market and in the hands of terrorists, 27 dictators wishing to better control their populace, warlords wishing to perpetrate ethnic 28 cleansing, etc. Autonomous weapons are ideal for tasks such as assassinations, 29 destabilizing nations, subduing populations and selectively killing a particular ethnic 30 group. We therefore believe that a military AI arms race would not be beneficial for 31 humanity. There are many ways in which AI can make battlefields safer for humans, 32 especially civilians, without creating new tools for killing people. 33 Just as most chemists and biologists have no interest in building chemical or biological 34 weapons, most AI researchers have no interest in building AI weapons -and do not 35 want others to tarnish their field by doing so, potentially creating a major public 36 backlash against AI that curtails its future societal benefits. Indeed, chemists and 37 biologists have broadly supported international agreements that have successfully 38 prohibited chemical and biological weapons, just as most physicists supported the 39 treaties banning space-based nuclear weapons and blinding laser weapons. 40

In summary, we believe that AI has great potential to benefit humanity in many ways, 41 and that the goal of the field should be to do so. Starting a military AI arms race is a bad 42 idea, and should be prevented by a ban on offensive autonomous weapons beyond 43 meaningful human control. 44

  Embargoed until 4PM EDT July 27 2015/5PM Buenos Aires/6AM July 28Sydney 1 This open letter will be officially announced at the opening of the IJCAI 2015 conference 2 on July 28, and we ask journalists not to write about it before then. Journalists who wish 3 to see the press release in advance of the embargo lifting may contact Toby Walsh. 4Hosting, signature verification and list management are supported by FLI; for 5 administrative questions about this letter, please contact tegmark@mit.edu.Autonomous weapons select and engage targets without human intervention. They 10 might include, for example, armed quadcopters that can search for and eliminate people 11 meeting certain pre-defined criteria, but do not include cruise missiles or remotely 12 piloted drones for which humans make all targeting decisions. Artificial Intelligence (AI) 13 technology has reached a point where the deployment of such systems is -practically 14 if not legally -feasible within years, not decades, and the stakes are high: autonomous 15 weapons have been described as the third revolution in warfare, after gunpowder and 16 nuclear arms. 17 Many arguments have been made for and against autonomous weapons, for example 18 that replacing human soldiers by machines is good by reducing casualties for the owner 19 but bad by thereby lowering the threshold for going to battle. The key question for 20 humanity today is whether to start a global AI arms race or to prevent it from starting. If 21 any major military power pushes ahead with AI weapon development, a global arms 22 race is virtually inevitable, and the endpoint of this technological trajectory is obvious: 23 autonomous weapons will become the Kalashnikovs of tomorrow. Unlike nuclear 24

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	8	Autonomous Weapons: An Open Letter from AI & Robotics
	9	Researchers 3

https://www.youtube.com/watch?v=9CO6M2HsoIA&feature=youtu.be

A series of papers published by an online publication ("The Intercept") details the drone assassination program of U.S. forces in Afghanistan, Yemen and Somalia. https://theintercept.com/drone-papers/

https://futureoflife.org/open-letter-autonomous-weapons/


Enrico Mingo Hoffman

Brice Clement

Chengxu Zhou

Nikos G Tsagarakis

Jean-Baptiste Mouret

Serena Ivaldi

Whole-Body Compliant Control of iCub: first results with OpenSoT

I. INTRODUCTION

Humanoid bipedal robots are becoming more and more popular among the research community thanks to their versatility in terms of locomotion and manipulation capabilities. On the other side, these platforms are, in general, more complex to control than other robotics platforms: locomotion in open and cluttered environments, balancing, whole-body control, redundancy resolution and perception are some of the still open topics in this field. Furthermore, it is very difficult to find mature hardware platforms to perform experiments. Several small-sized humanoids are actually available for purchasing but, for what concern medium (children-like) and large (adult-like) sizes, there are very few companies providing such products [START_REF] Kaneko | Humanoid robot hrp-4-humanoid robotics platform with lightweight and slim body[END_REF], [START_REF] Stasse | Talos: A new humanoid research platform targeted for industrial applications[END_REF]. Among the medium size platforms, the iCub [START_REF] Natale | The icub platform: a tool for studying intrinsically motivated learning[END_REF] is the one with the highest number of working units in the world. iCub is a humanoid robot with 53 degrees of freedom (DoF), originally conceived for research in cognitive and developmental robotics [START_REF] Natale | The icub platform: a tool for studying intrinsically motivated learning[END_REF]. After being equipped with force/torque and tactile sensors it became a central platform for whole-body dynamics estimation [START_REF] Fumagalli | Force feedback exploiting tactile and proximal force/torque sensing[END_REF], torque control and physical interaction [START_REF] Romano | The CoDyCo project achievements and beyond: Toward human aware whole-body controllers for physical human robot interaction[END_REF], enabling research in human-robot collaboration and assistance-oriented tasks [START_REF] Ivaldi | Anticipatory models of human movements and dynamics: the roadmap of the AnDy project[END_REF]. In these applications, locomotion, balancing and manipulation are complex and, in general, uncertainties are present due to the unstructured scenario and the presence of human agents. It is well known that such uncertainties can be handled with compliant controllers that permits to manipulate the environment or interact with humans in a safe way and without breaking the robot. In particular, it was shown that compliance plays a crucial role for the stabilization of bipeds when subject to external pushes [START_REF] Li | Stabilization for the compliant humanoid robot coman exploiting intrinsic and controlled compliance[END_REF].

In this work we present the integration of a simple compliant stabilizer [START_REF] Zhou | Stabilization of bipedal walking based on compliance control[END_REF], which acts at the level of the Center of Mass (CoM), together with a whole-body controller [START_REF] Hoffman | Robot control for dummies: Insights and examples using opensot[END_REF], in the iCub robot. This integration effort has been made in order to provide iCub with a simple but effective module which can handle whole-body tasks and stabilization at the same time. [START_REF] Zhou | Stabilization of bipedal walking based on compliance control[END_REF] and uses a simplified model to stabilize the robot at the CoM. The stabilization is done computing a control input, consisting in a correction of the reference of the CoM, based on the desired vs actual Zero Moment Point (ZMP) position as well as its velocity. Equivalent stiffness and damping gains are used to tune the controller. The actual position of the ZMP is measured using the force/torque sensors at the ankles of iCub. This well known solution is very effective because when the robot has the feet in contact with the environment, the force/torque sensors measure all the possible interactions which the robot is subject to. The corrections at the level of the CoM are added to the CoM reference and sent, with all the other Cartesian references, to the Whole-Body IK Engine. This component is based on the work in [START_REF] Hoffman | Robot control for dummies: Insights and examples using opensot[END_REF] and permits to handle priorities between tasks as well as constraints in form of equalities and inequalities. In particular, the Whole-Body IK Engine computes, from the Cartesian references, joint level velocities which are integrated and sent to the robot low level PID controllers. OpenSoT permits to easily customize the controller in terms of formulation, tasks/constraints organization and solver. In particular in this wok we have chosen the velocity control formulation, the tasks are organized in the following priorities (in particular we are considering a floating-base robot):

         World TRFoot + World TLFoot / World T XY CoM + World T ZO Waits + TNeck / World TRArm + World TLArm / T Posture          << C Joint Limits + CJoint Velocity

Limits

(1) where a T X b represents the X component of a velocity task from frame a to frame b expressed in frame a (if frame a is missing, the task is intended in joint space), and C represents a constraint. the formulation in [START_REF] Kaneko | Humanoid robot hrp-4-humanoid robotics platform with lightweight and slim body[END_REF] shows how the tasks and constraints are organized. In particular the symbol "\" is used to set HARD priority (in terms of nullspace projection) relation between tasks while the "+" symbols is used to set SOFT priority (in terms of augmentation) between them. Such "stack" enables to move the arms ensuring that the CoM will not change its position, the stabilizer behaves in order to track a desired ZMP reference changing the reference of the CoM. Despite in this work a fixed position for the ZMP is considered, the same control architecture has been used in other robots to realize walking behaviours. Figure 1 shows the implemented control scheme.

III. EXPERIMENTS

We consider three set of experiments where a human applies external forces to various parts of the body of the robot.

In the first experiment the robot is balancing while an operator tele-operates the hand end-effectors. The level of compliance is given primarily by the equivalent stiffness and damping gains of the stabilizer. On the other side, the motion behaviour is given by the parameters of the IK engine. The second task consists in squatting motions: here we observed that high compliance along the X axis generates high oscillatory motions, therefore we reduced the gains in the X direction of the stabilizer (which correspond to increasing the stiffness). Finally we consider a typical service task: carrying an empty plastic cup on a tray. Here, we want that the robot to be able to stabilize the object while being subject to external perturbations. The motion behaviour given by (1) permits to stabilize the end-effectors. Of course, considering the order of priority in (1), the arms will move in a way to do not disturb the CoM tracking from the previous task. This is compatible with human-like behaviours: if the robot is subject to very high external perturbation, the main task to satisfy is the balancing (with any limb) than the stability of the plastic cup. Figure 2 shows the setup of the aforementioned experiments, a video is available at https://www.youtube.com/watch?v=W6ug-wtAfTk.

Fig. 1 :

 1 Fig. 1: Proposed control scheme: xd contains all the desired references from the user side. The CoM reference is modified by the Compliant Stabilizer which generates a ∆xCoM according to the desired position of the ZMP (namely ZMPd) and the measured wrenches from the feet wLF and wRF. The updated references x * d are sent to the Whole-Body IK Engine and converted to joint velocities qd. The output joint velocities are integrated and sent to the joint position PID controllers.

Fig. 2 :

 2 Fig. 2: Experiments performed on iCub using the proposed wholebody control structure.

  This research was supported by European Union Horizon 2020 robotics program [ICT-23-2014], grant agreement n.731540 AnDy and grant agreement n.644727 CogIMon, by the European Research Council (grant agreement n.637972 ResiBots, and by the Region Grand Est (project "LocoLearn"). {brice.clement, jean-baptiste.mouret, serena.ivaldi}@inria.fr II. COMPLIANT WHOLE-BODY CONTROLLER The compliant whole-body controller is divided in two main components: the Compliant Stabilizer and the Whole-Body Inverse Kinematics (IK) Engine. The Compliant Stabilizer is based on the work in

1

Advanced Robotics Department, Italian Institute of Technology, Italy, Genoa, {enrico.mingo, chengxu.zhou, nikos.tsagarakis}@iit.it 2 Inria, CNRS, Université de Lorraine, LORIA, France, Nancy,

In this work a simple, yet effective, whole-body compliant stabilizer for the humanoid robot iCub has been presented. The control architecture is based on the feed-back controller in [cite ChenXue] and the whole-body IK in[START_REF] Hoffman | Robot control for dummies: Insights and examples using opensot[END_REF]. Experiments of such control architectures have been demonstrated for squatting motions, tele-operation and bimanual-manipulation, all taking into account rejection of external disturbances. An open-source implementation of the compliant stabilizer is available at https://github.com/ADVRHumanoids/compliant stabilizer, the OpenSoT library is available at https://github.com/ robotology/OpenSoT. Future works will integrate walking with manipulation and tele-operation with learning[START_REF] Spitz | Trial-and-error learning of repulsors for humanoid qp-based whole-body control[END_REF].


Jean-Nicolas Proulx
email: jean-nicolas.proulx.1@ulaval.ca

Keywords: Digital game based learning, DGBL, education, flow, game mechanics, learning mechanics, LM-GM, LMGM-SDT, motivation, self-determination theory, SDT

Educational technologies and motivation

One of the persistent educational myths is that technology fosters learners' motivation by itself [START_REF] Kleiman | Myths and realities about technology in K-12 schools[END_REF]. Educational psychologists have issued warnings about the assumption that the use of new technologies in education, such as digital games, causes an increase in students' motivation [START_REF] Amadieu | Apprendre avec le numérique: mythes et réalités[END_REF][START_REF] De Bruyckere | Urban myths about learning and education[END_REF]. While in some studies, there seemed to be a link between the integration of technology in education and the learners' motivation [START_REF] Papastergiou | Digital game-based learning in high school computer science education: Impact on educational effectiveness and student motivation[END_REF], in other research, that impact was not observed (e.g., [START_REF] Kebritchi | The effects of modern mathematics computer games on mathematics achievement and class motivation[END_REF]. Furthermore, [START_REF] Bitner | Integrating technology into the classroom: Eight keys to success[END_REF] stressed the importance of the integration of technology into the curriculum as what matters the most when studying the technology effects on educational contexts. In other words, the technology itself does not necessarily foster student motivation [START_REF] Kleiman | Myths and realities about technology in K-12 schools[END_REF], but the activities supported by technology could have the potential to have a positive impact on students' motivation [START_REF] Price | E-motivation! The role of popular technology in student motivation and retention[END_REF]. According to the literature review of Huang, Huang and Tschopp (2010, p. 790), " [motivation] is the essential element to initiate and sustain learning and performance". Hence, technology enhanced learning (TEL) activities need to be designed in a way that "provide adequate level of motivational stimuli" (Huang et al., 2010, p. 790). The study of the relation between technology and motivation is also present in the field of Digital Game Based Learning (DGBL) [START_REF] Huang | Sustaining iterative game playing processes in DGBL: The relationship between motivational processing and outcome processing[END_REF] and according to Whitton (2010, p. 596), it is also often assumed by researchers and practitioners that "computer games are intrinsically motivational for most, if not all, people". Outcomes pertaining to research in DGBL have led to inconsistent results related to the link between motivation and the use of digital games in education [START_REF] Filsecker | A multilevel analysis of the effects of external rewards on elementary students' motivation, engagement and learning in an educational game[END_REF]. Furthermore, [START_REF] Kebritchi | The effects of modern mathematics computer games on mathematics achievement and class motivation[END_REF] discovered in their literature review on instructional games and motivation that only four of their 16 studies indicated that learners showed a motivational increase from using serious games. Also, according to Kebritchi et al. (2010, p. 428), "the studies mostly used mathematics games as treatments, achievement and motivation as dependent variables, and prior knowledge, computer experience, and language background as independent variables" (p. 428). In their literature review, instructional games were mostly used in mathematics and the student level varied from elementary to higher education.

We interpret the inconsistent results between DGBL and motivation according to two main factors. Firstly, DGBL integration in the classroom is a complex situation that might involve factors from multiple perspectives and levels (the learners' diversity, the teacher, the game and its mechanics, the DGBL integration in the learning situation, etc.). These diversity of factors, perspectives and levels of analysis result in a complex analysis of the effects of DGBL on motivation; multiple factors, which are not necessarily related to the digital game, such as teacher support and evaluation context, could mediate the relation between DGBL and the learners' motivation. Secondly, digital games in education come in great variety depending on their goals and inherent mechanics, which means that they can hardly be studied as a whole. In the same game, some mechanics could be associated with motivational effects for certain students in a certain context (e.g. competition as a motivational aspect among high confidence students), while other mechanics could even hinder motivation (e.g. collecting points could be perceived as boring). According to [START_REF] Squire | Video Games and Learning: Teaching and Participatory Culture in the Digital Age[END_REF], good games have the potential to inspire interest, creativity, and social interaction. However, there are also games that, despite their intentions to be educational and playful, fail to engage certain types of learners.

In order to find out what makes a digital game achieve its educational and playfulness objectives, we make the hypothesis that we should consider the analysis of DGBL on a microlevel (considering the learning mechanics and game mechanics) rather than on a macro level (considering the game as a whole). The micro-level approach aims to study the DGBL according to the specific learning and games mechanics, which constitute the game. In order to achieve this aim, we define two specific research questions:

 Research question 1 (RQ1): What are the self-determination components related to the learners' motivation in a context of DGBL?  Research question 2 (RQ2): How the self-determination components can be operationalized through the Learning Mechanics and Game Mechanics (LM-GM) model?

In order to answer these two research questions, the following sections provide a review of the literature that analyzes the DGBL effects on students' motivation at different levels of education. After reviewing these studies, we introduce the self-determination theory (SDT, Ryan & Deci, 2000b) as one of the most complete and actual motivation theories in the social sciences. We introduce the rationale of choosing the SDT for the analysis of DGBL. Finally, we analyze SDT components according to their links with the LM-GM model [START_REF] Lim | Strategies for effective digital games development and implementation[END_REF].

Digital game based learning and motivation

The existing body of research that analyzed the use of digital games in education generally shows an overall positive link between the use of DGBL and their motivational effects among the learners [START_REF] Groff | The impact of console games in the classroom: Evidence from schools in Scotland[END_REF][START_REF] Gros | Digital games in education: The design of games-based learning environments[END_REF][START_REF] Ritzko | Using games to increase active learning[END_REF]. Over a review of more than 70 empirical research studies on the use of digital games in the classroom from [START_REF] Connolly | A systematic literature review of empirical evidence on computer games and serious games[END_REF], the most observed positive outcomes were affectivity and motivation (n = 33) and learning (n = 32). Also, according to [START_REF] Wouters | A metaanalysis of the cognitive and motivational effects of serious games[END_REF], the potential of games to foster intrinsic motivation is mentioned in much research. Players are willing to spend more time and energy to complete an activity when they see it as fun [START_REF] Wouters | A metaanalysis of the cognitive and motivational effects of serious games[END_REF]. In addition, two of the key factors that are associated with digital games are autonomy (possibility to make relevant choices) and competence (the task represents a challenge while being achievable), which are core components of the self-determination theory that positively influence motivation [START_REF] Wouters | A metaanalysis of the cognitive and motivational effects of serious games[END_REF].

Aside from motivation, games also demonstrate the potential to inspire interest, creativity and social interactions from students [START_REF] Squire | Video Games and Learning: Teaching and Participatory Culture in the Digital Age[END_REF]. Although he sees great potential in the use of games in education, Squire (2005, p. 4) indicates that many contextual elements need to be taken into account for motivation to emerge: "motivation for the gamers in my study was thus not simply a 'property' or variable that they either had or did not have; motivation emerged through the intersection of students' goals and life histories, the game's affordances, and the institutional context". In his experiment, [START_REF] Squire | Changing the game: What happens when video games enter the classroom[END_REF] attributes the appearance of motivation to a coherence between these many contextual elements and not only to the use of a game itself. Also, according to Filsecker and Hickey (2014, p. 138), "the kinds of cognitive strategies used might well be what distinguish motivated and unmotivated learners" [START_REF] Renkl | Learning from worked-out examples: A study on individual differences[END_REF]. Those inconsistencies lead us to conclude that not every game experience is perceived as fun by the players. Those inconsistencies also bring the hypothesis that digital games have indeed the potential to foster motivation and learning, but that this potential might not turn into a concrete form if certain requirements regarding the game, the learners and the learning context are not met. [START_REF] Wouters | A metaanalysis of the cognitive and motivational effects of serious games[END_REF] suggested that games that are considered as motivating by the players may lead to a greater engagement in the game play from them. This is why including a motivational theory, such as self-determination theory (SDT, Ryan & Deci, 2000b), in the game design and educational integration might increase the probabilities that the game would be considered 'fun' by the players. By examining digital games under the perspective of SDT, we might find clues on the different elements that games could include in their design in order to increase their motivational potential. Since we inspect the game design on a micro level, the LM-GM model from [START_REF] Lim | Strategies for effective digital games development and implementation[END_REF] will be used, because it analyzes serious games according to their respective Learning Mechanics and Game Mechanics.

In the next sections, we will analyze the LM-GM model under the self-determination perspective in order to identify the self-determination components related to the learners' motivation in a context of DGBL (RQ1) and to evaluate the self-determination components that can be operationalized through the Learning Mechanics and Game Mechanics (LM-GM) model (RQ2). Based on these analyses, we propose a theoretical framework allying LM-GM and SDT that could help to explore the underlying components that map the use of digital games to the motivational effects. The goal behind the combination of SDT and LM-GM would be to overcome the motivational limits of games and to contribute to a better analysis of the motivational factors of DGBL. In the next section, we will review the SDT as well as its main components.

Self-determination theory (SDT) and motivation

Motivation theories in DGBL

Two of the common motivational theories used with DGBL are the flow experience (e.g. [START_REF] Chen | Improving learning achievements, motivations and flow with a progressive prompt-based mobile gaming approach[END_REF][START_REF] Franciosi | A comparison of computer game and language-learning task design using flow theory[END_REF][START_REF] Garris | Games, motivation, and learning: A research and practice model[END_REF][START_REF] Kiili | Flow framework for analyzing the quality of educational games[END_REF][START_REF] Su | Developing and evaluating gamifying learning system by using flow-based model[END_REF] and the Cognitive Evaluation Theory with the use of intrinsic motivation (e.g. [START_REF] Erhel | Digital game-based learning: Impact of instructions and feedback on motivation and learning effectiveness[END_REF][START_REF] Huang | Sustaining iterative game playing processes in DGBL: The relationship between motivational processing and outcome processing[END_REF][START_REF] Yang | Building virtual cities, inspiring intelligent citizens: Digital games for developing students' problem solving and learning motivation[END_REF].

The flow theory features a concept called the flow experience which was introduced by [START_REF] Csikszentmihalyi | Play and intrinsic rewards[END_REF]. According to [START_REF] Csikszentmihalyi | Flow: The psychology of optimal experience[END_REF], this concept can be defined as a complete state of cognitive absorption or engagement in a task, in which the individual is not affected by thoughts or emotions unrelated to the task. [START_REF] Kiili | Flow framework for analyzing the quality of educational games[END_REF] extended the flow theory to include playability and demonstrated the potential of the flow theory in evaluating the quality of educational games.

The Cognitive Evaluation Theory from Ryan and Deci (2000a) categorizes motivation into two different types: intrinsic motivation and extrinsic motivation. "The concept of intrinsic motivation (IM) refers to behaviours performed out of interest and enjoyment. In contrast, extrinsic motivation (EM) pertains to behaviors carried out to attain contingent outcomes (Deci, 1971)" (Vallerand & Ratelle, 2002, p. 37).

Intrinsic motivation is often used to affirm that games in educational contexts are fun (e.g., [START_REF] Liu | A study of learning and motivation in a new media enriched environment for middle school science[END_REF]. Using a theory such as SDT instead of CET to evaluate motivation in games could be very beneficial since, as we can see in Ryan and Deci (2000b), SDT is a meta-theory that englobes CET. In the next sections, we introduce SDT and its advantages in relation to the CET model.

Self-determination theory (SDT)

While SDT is different from the Cognitive Evaluation Theory and the Flow Theory, it does share common ground with both of them. Both [START_REF] Kowal | Motivational determinants of flow: Contributions from selfdetermination theory[END_REF] and [START_REF] Lee | The Relationship of Motivation and Flow Experience to Academic Procrastination in University Students[END_REF] were able to establish a link between SDT and the flow theory. In [START_REF] Kowal | Motivational determinants of flow: Contributions from selfdetermination theory[END_REF], participants who had a self-determined motivation reached the highest states of flow experience. In [START_REF] Lee | The Relationship of Motivation and Flow Experience to Academic Procrastination in University Students[END_REF], students with higher self-determined motivation were more likely to reach the flow experience and to be deeply engaged in their task.

SDT is also linked to Cognitive Evaluation Theory (CET), since SDT was created with the CET as its core. [START_REF] Deci | The "what" and "why" of goal pursuits: Human needs and the self-determination of behavior[END_REF] decided to combine CET with three other motivational theories in order to make a motivational theory that would be more complete. That new theory of motivation developed by psychologists [START_REF] Deci | The "what" and "why" of goal pursuits: Human needs and the self-determination of behavior[END_REF] was given the name Self-Determination Theory and is described as the following: "Self-determination theory focuses on the dialectic between the active, growth-oriented human organism and social contexts that either support or undermine people's attempts to master and integrate their experiences into a coherent sense of self" (Deci & Ryan, 2004, p. 27). In order to achieve growth, people should be able to develop the three following needs: autonomy, competence and relatedness.

Autonomy. This concept refers to the ability "to self-organize and regulate one's own behavior (and avoid heteronomous control), which includes the tendency to work toward inner coherence and integration among regulatory demands and goals" (Deci & Ryan, 2000, p. 252). In other words, it refers to "being the perceived origin or source of one's own behavior" (Deci & Ryan, 2004, p. 8).

Connection or relatedness. This concept refers to the need for belonging and attachment to other people as we can see in the next two definitions. "To seek attachments and experience feelings of security, belongingness, and intimacy with others" (Deci & Ryan, 2000, p. 252). "Relatedness refers to feeling connected to others, to caring for and being cared for by those others, to having a sense of belongingness both with other individuals and with one's community (Beaumeister & Leary, 1995;Bowlby, 1979;Harlow, 1958;Ryan, 1995)" (Deci & Ryan, 2004, p. 7). Also, that need for relatedness concerns the sense of being with others and is not fueled by the attainment of specific outcomes such as social status gain [START_REF] Deci | Overview of self-determination theory: An organismic dialectical perspective[END_REF].

Competence. According to Deci and Ryan (2000, p. 252), competence means "to engage optimal challenges and experience mastery or effectance in the physical and social worlds". Using [START_REF] White | Motivation reconsidered: the concept of competence[END_REF] definition of effectance, Klimmt & Hartmann (2006, p. 137) defined this concept as "the satisfaction of having imposed an effect on the environment". Within SDT, the concept of competence means that individuals are seeking the pleasure of being effective with what they engage themselves (Deci & Ryan, 2000). That need for competence would lead people to "seek challenges that are optimal for their capacities and to persistently attempt to maintain and enhance those skills and capacities through activity" (Deci & Ryan, 2004, p. 7).

Those three components represent key elements that can positively influence intrinsic motivation as well as autonomous motivation (Deci & Ryan, 2000). We will further elaborate on the different types of motivation in the next section.

The continuum of autonomy and motivation within SDT

Within SDT, different types of motivation are distinguished depending on the reasons or goals that are related to the emergence of a new action (Ryan & Deci, 2000b). According to this theory, "motivation concerns energy, direction, persistence and equifinality-all aspects of activation and intention" (Ryan & Deci, 2000b, p. 69). Then, motivation is divided into three categories (see Figure 1). Lack of motivation is referred to as amotivation [START_REF] Ryan | Promoting self-determined school engagement : Motivation, learning and well-being[END_REF]. Intrinsic motivation "refers to doing something because it is inherently interesting or enjoyable" (Ryan & Deci, 2000a, p. 55) and Extrinsic motivation "refers to doing something because it leads to a separable outcome" (Ryan & Deci, 2000a, p. 55).

In order for learners to feel self-determined within an educational context, we can assume that they would need to develop a certain level of competence, connections with other people (peers, teacher/s, parent/s…) within a certain margin of autonomy that would allow them to feel in control of the learning task and their activities within the task. To that, we can add a coherence between the task and students' own goals.

SDT in education

According to the literature review of [START_REF] Reeve | Self-determination theory applied to educational settings[END_REF], the autonomy of learners is related to their well-being in the classroom. The literature review of [START_REF] Reeve | Self-determination theory applied to educational settings[END_REF] also indicates that students benefit from having a teacher who allows them to have a certain level of autonomy in the Table 1. Continuum in the self-determination according to the type of regulation and autonomy [START_REF] Ryan | Promoting self-determined school engagement : Motivation, learning and well-being[END_REF] classroom, which seems to allow students to develop their orientation to grow, feel more competent, more confident, more open-minded and creative.

Almost a decade after proposing the SDT, Ryan and Deci (2009, p. 179) highlighted a positive link between teachers that facilitate a certain degree of autonomy and the degree of engagement and motivation felt by learners: "various studies of elementary and high school students (e.g., Hardre & Reeve, 2003;Jang, Reeve & Deci, 2007;Skinner & Belmont, 1993) have shown that teachers' autonomy support is related to students' autonomous motivation and engagement". So, these results are showing the possibility that students' perception of autonomy can be influenced by how 'autonomy supportive' their teacher is.

A literature review was performed by [START_REF] Taylor | A self-determination theory approach to predicting school achievement over time: the unique role of intrinsic motivation[END_REF] on 18 studies that "assessed the relation of motivation types according to SDT to school achievement" (Taylor et al., 2014, p. 344) by using the Academic Motivation Scale (AMS) from [START_REF] Vallerand | Intrinsic, extrinsic, and amotivational styles as predictors of behavior: A prospective study[END_REF]. That literature review pointed out a relation between STD and academic achievement. In secondary education, Taylor et al. (2014, p. 345) found out that "intrinsic motivation and identified regulation have a moderately strong, positive relation with school achievement". Also, they observed that "introjected and external regulation had a weaker, but significant negative relation with school achievement" (Taylor et al., 2014, p. 345) and that "amotivation had a strong, negative relation to school achievement" (Taylor et al., 2014, p. 345).

SDT within digital games

According to Katz and Assor (2007, p. 439), a game is more likely to be considered 'fun' by students if they can relate to their personal life with the game or if they are offered meaningful choices that take into consideration their "needs, interests, goals, ability and cultural background". That led us to believe that we may be able to increase the odds that a game is considered motivating by integrating certain components into the game mechanics. According to SDT, those elements would be autonomy, competence, relatedness and a coherence between game's goals and students'.

In a context where we are working to foster very specific components within the mechanics of the game, a model such as the LM-GM can come in very handy. A model such as LM-GM that looks at a serious game design at a micro level becomes useful, since it gives the possibility to use game mechanics in a way that fosters SDT components.

The learning mechanics game mechanics model and game design

In this section, we describe the LM-GM model from [START_REF] Lim | Strategies for effective digital games development and implementation[END_REF] and how it can be used in game design. After that, we analyze its impact on games on which it has been used so far.

Introduction to LM-GM model

There is a lack of robust methodology that informs the correlation between mechanics related to learning and playing, which introduces a huge challenge in guaranteeing efficacy and success [START_REF] Arnab | Mapping learning and game mechanics for serious games analysis[END_REF][START_REF] Arnab | Towards a trans-disciplinary methodology for a game-based intervention development process[END_REF][START_REF] Garris | Games, motivation, and learning: A research and practice model[END_REF][START_REF] Wilson | Relationships between game attributes and learning outcomes review and research proposals[END_REF]. Existing frameworks for designing serious games "do not specifically target the analysis of the relationships between game mechanics and learning constructs, which is a key factor in the game design for learning" (Arnab et al., 2015, p. 394).

To map pedagogical constructs to entertaining gameplay, [START_REF] Lim | Strategies for effective digital games development and implementation[END_REF] proposed the LM-GM and was evaluated by [START_REF] Arnab | Mapping learning and game mechanics for serious games analysis[END_REF] as a Serious Game (SG) analysis guideline with positive outcomes. As a general overview, the pedagogical elements are viewed as an abstract interface while game elements are deemed as a concrete interface of SGs. This means that pedagogy and its methods are abstract (theoretical and conceptual), while game mechanics are concrete, i.e. by rules or algorithms. Figure 2 lists an example of the learning mechanics (LMs) and the game mechanics (GMs). The mechanics that support both learning and games are regarded as abstract or concrete, depending on the mode of operation, where the abstract elements are more conceptual and the concrete counterparts are more explicit and implementable. The concrete mechanisms enable abstract concepts to work. The model is, however, descriptive and not prescriptive, "in the sense that it allows its users to freely relate learning and gaming mechanics to describe SG situations by drawing a map and filling a table" (Arnab et al., 2015, p. 396).

What we know so far of the impact of games designed using the LM-GM model in education

The LM-GM model can be used to either aid SG design or game analysis, where it provides a concise means to map how ludic elements link to pedagogic intent directly based on a player's actions and game play, i.e. SG mechanics. [START_REF] Arnab | Mapping learning and game mechanics for serious games analysis[END_REF] For instance, the model has been used to develop the design of the PR:EPARe gamea game to aid discourse on relationships and sexual pressure and coercion within a classroom setting [START_REF] Arnab | The development approach of a pedagogically-driven serious game to support Relationship and Sex Education (RSE) within a classroom setting[END_REF], where the abstract and concrete aspects of learning mechanics and game mechanics were used to identify the pedagogic delivery style that the game should take. Efficacy testing of the game solution was validated through a cluster-randomized controlled trial in local schools in the UK (n = 505), which indicated positive outcomes in favour of the game-based approach. Those results are based on self-reported measures of psychosocial preparedness for avoiding coercion, as well as the observation data on students and teachers experience through the process.

This evaluation of PR:EPARe has also led to the development of a more transdisciplinary methodology for game-based intervention design, where the different considerations taken to develop the game were infused [START_REF] Arnab | Towards a trans-disciplinary methodology for a game-based intervention development process[END_REF]. The LM-GM model is merged with the Mechanics-Dynamics-Aesthetics (MDA, [START_REF] Hunicke | MDA: A formal approach to game design and game research[END_REF]) model, commonly used for entertainment games in order to marry the pedagogical (serious) aspects with the entertainment attributes of gameplay.

The LM-GM model has also been included as an important component towards informing the design of stealth assessment via a serious game within an intelligent scaffolding infrastructure [START_REF] Baalsrud-Hauge | Learning through analytics architecture to scaffold learning experience through technology-based methods[END_REF], demonstrating the potential impact of LM-GM to inform stealth learning analytics via game mechanics. Reusability is, however, a key challenge due to the fact that serious games can be bespoke and not immediately re-purposable [START_REF] Arnab | Towards a trans-disciplinary methodology for a game-based intervention development process[END_REF]. Towards addressing this issue, Baalsrud- [START_REF] Hauge | Serious Games Mechanics and oppotunities for reuse[END_REF] analysed the mapping of the learning and game mechanics of two serious games -PR:EPARe and Seconds, which led to the definition of key SGM reuse principles, which demonstrated how the LM-GM model can be used to inform the analysis of serious game mechanics (SGM) towards identifying reusability.

The game and learning mechanics under the perspective of the Selfdetermination theory (SDT)

Game mechanics involve a certain degree of interaction with the game artifact or the other players that are involved in the game activity. As a mechanism of interaction, 'retroactivity' represents a key aspect of the game mechanics that allows players to move forward within the game [START_REF] Koster | Theory of fun for game design[END_REF]. Interactions with a game involve some degree of extrinsic motivation based on its retroactions. This characteristic has been highlighted by Dunwell, de Freitas and Jarvis (2011) as a positive aspect of games. A deep engagement of the player-learners inside the game universe can be interpreted as a sign that digital games used can also promote the emergence of autonomous motivation. The game and learning mechanics analysis under the perspective of the Self-determination theory (SDT) displayed in Figure 3, was developed with the intention of getting a bigger picture of the LMGM-SDT framework and advance the study of motivational factors in DGBL. The framework was created by mixing together the LM-GM classification based on Bloom's taxonomy from [START_REF] Arnab | Mapping learning and game mechanics for serious games analysis[END_REF] and the self-determination continuum from [START_REF] Ryan | Promoting self-determined school engagement : Motivation, learning and well-being[END_REF]. The rationale behind that framework was to identify and analyze the game mechanics (intending to support the game experience) and learning mechanics (intending to support the learning activity) with a potential to support motivation. In order to analyse the LM-GM under the lens of SDT, we identified the learning mechanics and game mechanics that seemed to have a clearer link with the SDT components of the selfdetermination continuum from [START_REF] Ryan | Promoting self-determined school engagement : Motivation, learning and well-being[END_REF], which ranges from amotivation, to extrinsic motivation and ends with intrinsic motivation as its highest level (represented in Figure 3 in the horizontal axis). In the vertical axis, Figure 3 is organized on the Bloom's taxonomy (last column in bold), from retention at the lowest level and creating in the higher level. The central columns display the game mechanics (left) and learning mechanics (right, grey color and italic) according to their extrinsic and intrinsic motivation potential.

Since games always provide a certain level of interaction with the game artifact or the other players [START_REF] Koster | Theory of fun for game design[END_REF], none of the game nor learning mechanics were placed into the amotivation zone of the table. the learning outcomes and motivational potential will depend on the implementation of the mechanics within the learning situation where the game is integrated. This means the same mechanic can be placed in different places depending on the concrete form it takes within the game environment. By classifying them both on selfdetermination continuum and on Bloom's ordered thinking skills, it becomes possible to evaluate the pedagogical outcome as well as the motivational potential of the mechanics that are used within the game.

Due to the neutral aspect or their motivational potential, some game and learning mechanics of the LM-GM model, such as the meta-game mechanic and realism mechanic, cannot be classified. The LM-GM model was based on Bloom's learning theory in [START_REF] Arnab | Mapping learning and game mechanics for serious games analysis[END_REF] in order to introduce a taxonomy to classify the different Learning Mechanics and Game Mechanics. That way, game mechanics and learning mechanics can be used in a way that fosters very specific thinking skills and to make sure that the game as a whole remains an efficient learning tool. The same principle can also apply to motivation by exploiting game mechanics and learning mechanics in a way that fosters SDT components. In addition, considerable attention should be paid to the context and manner in which the different mechanics are integrated within the game, since it can affect the SDT continuum perception of the learner. For instance, the game mechanic 'progression' can be in both the second and fourth columns, because depending on how this mechanic is used, it can be perceived by the student as a controlling mechanic (e.g. If every student has to go through A, B, C, D and E in that specific order to reach F) or an autonomy supporting mechanic (e.g. If students are suggested a certain pathway by the game in order to reach F, but they are the one deciding how they want to reach F).

Discussion

In this paper, we introduce a framework that combines both a game design model and a motivational theory. That framework aims to improve the motivational potential of games by allowing learners to develop their autonomous motivation.

Our first research question (RQ1) aimed to identify the self-determination components related to learners' motivation in a context of DGBL. In order to develop the learners' motivation, specifically autonomous motivation, which has been related to a positive impact on academic achievement [START_REF] Taylor | A self-determination theory approach to predicting school achievement over time: the unique role of intrinsic motivation[END_REF] and engagement [START_REF] Lee | The Relationship of Motivation and Flow Experience to Academic Procrastination in University Students[END_REF], the game based learning activity should be in coherence with four requirements of the player activity: the students' goals (requirement 1), students' feeling of autonomy (requirement 2), competence (requirement 3) and relatedness (requirement 4) within the game. According to SDT, if these four requirements are met, then the game should foster autonomous motivation.

Table 2. Game mechanics (left) and learning mechanics (right, grey color and italic) according to their extrinsic and intrinsic motivation potential.

Our second research question (RQ2) aimed to find a way to operationalize SDT components through the Learning Mechanics and Game Mechanics (LM-GM) model. As we saw in Figure 3, the LM-GM model aims to facilitate the operationalization of the game mechanics in a way that can foster different motivation types. In order to foster SDT components, a special attention needs to be paid to the nature of the mechanics that are used (which mechanics), since different mechanics will have different impacts. Also, as we saw in the previous section with the game mechanic progression, the way that mechanics are operationalized within the game can also have an impact on how they are perceived by players.

Analyzing the LM-GM from the perspective of SDT shows the need to evaluate the learning mechanics and game mechanics in the context they are used. For instance, a game designed with a game mechanic (e.g. communal discovery) could foster the player-learners' autonomy and self-determination. If there is too much external regulation from the instructional support provided by the teacher, the learners' perception of autonomy may be reduced. On the other hand, a game with mechanics that only promotes externally controlled motivation could be integrated in a more challenging task with higher degrees of autonomy (e.g. meaningful choices) and team engagement. In all cases, we should consider having a balance between many factors such as the level of external and intrinsic motivation provided by the digital game, the learners' level of competence and their regulation and co-regulation capabilities to avoid under and over regulation situations [START_REF] Romero | Internal and external regulation to support knowledge construction and convergence in computer supported collaborative learning (CSCL)[END_REF], which could end up having a negative impact on the learners' motivation and engagement. The framework we assembled combines both the self-determination motivational theory and the LM-GM framework. While both SDT and LM-GM seems individually promising, we have yet to explore their compatibility. In order to investigate the compatibility and the pedagogical relevance of the framework, we still need to experiment it in a concrete class setting. However, the present paper does bring forward evidences that a game design that meets the four requirements of the player activity should contribute to enjoyment, motivation and engagement.

Figure 1 .

 1 Figure1. The LM-GM mapping framework[START_REF] Arnab | Mapping learning and game mechanics for serious games analysis[END_REF]

Acknowledgments

We would like to thank the reviewers and Nicola Whitton for their thorough reviews and, in particular, for their constructive critique and suggestions for improvement.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Author Biographies

Jean-Nicolas Proulx is a PhD student in Educational Technologies and an Auxiliary Teacher in the Bachelor's Degree in Education and Primary Education program (BEPEP) at Université Laval. His main research interests are serious games, academic motivation, and academic success with a special focus on Game Based Learning. He is currently doing his thesis on the usage of Game Based Learning in order to increase academic success and motivation in high school history class setting.

Margarida Romero is professor of educational technology at Université Laval (Canada). Her research is oriented towards the inclusive, humanistic and creative uses of technologies (creative design and programming of games and educational robots) for the development of the 21st century skills across the lifespan: cooperation and communication, problem solving, creativity and computational thinking. She wrote the children's' book "Vibot the robot" aiming to demystify programming and robotics for children, their parents and their teachers.

Dr. Sylvester Arnab is a Reader in Games Science at Coventry University, UK co-leading research at the Disruptive Media Learning Lab (DMLL). With more than 10 years research experience in simulation, serious games and gamification combined, his research interests include gameful, playful and pervasive designs that transform ordinary tasks into extraordinary experiences. As lead of the Game Science research at the DMLL, Sylvester is also leading the EU Horizon 2020 BEACONING project (worth €5.9 million with 15 partners), which aims to foster 'anytime anywhere' learning using pervasive, context-aware and gamified techniques.