Rules offer a convenient means of limiting the operational scope of our AI programs so as to not transgress predictable moral boundaries. Yet the imposition of an operational morality based on mere rules will not turn our machines into moral agents, just the unthinking tools of moral designers. If we are to imbue our machines with a profound functional morality, we must first gift them with a moral imagination, for empathic morality — where one agent treats another as it would want to be treated itself — requires an ability to project oneself into the realms of the counterfactual. In this paper we thus explore the role of the moral imagination in generating new and inspiring stories. The creation of novel tales with a built-in moral requires that an artificial system possess the ability to guess at the morality of characters and their actions in novel settings and events. Our moralizing tale-spinner — which generates Aesop-style tales about human-like animals with identifiable human qualities — also faces another challenge: it must render these tales as micro-texts that can be distributed as tweets. As we shall also use metaphor to lend elasticity to our moral conceptions, these short stories, rich in animal metaphors, will comprise part of the daily output of the @MetaphorMagnet Twitterbot.

We report here on our previous research on the evolution of commitment behaviour in the one-off and iterated prisoner's dilemma and relate it to the issue of designing non-human autonomous online systems. We show that it was necessary to introduce an apology/forgiveness mechanism in the iterated case since without this restorative mechanism strategies evolve that take revenge when the agreement fails. As before in online interaction systems, apology and forgiveness seem to provide important mechanisms to repair trust. As such, these result provide, next to the insight into our own moral and ethical considerations, ideas into how (and also why) similar mechanisms can be designed into the repertoire of actions that can be taken by non-human autonomous agents.

Humans need morality and ethics to get along constructively as members of the same society. As we face the prospect of robots taking a larger role in society, we need to consider how they, too, should behave toward other members of society. To the extent that robots will be able to act as agents in their own right, as opposed to being simply tools controlled by humans, they will need to behave according to some moral and ethical principles. Inspired by recent research on the cognitive science of human morality, we take steps toward an architecture for morality and ethics in robots. As in humans, there is a rapid intuitive response to the current situation. Reasoned reflection takes place at a slower time-scale, and is focused more on constructing a justification than on revising the reaction. However, there is a yet slower process of social interaction, in which examples of moral judgments and their justifications influence the moral development both of individuals and of the society as a whole. This moral architecture is illustrated by several examples, including identifying research results that will be necessary for the architecture to be implemented.

This paper and use of autonomous weapons systems has been will discuss the moral and ethical questions that arise in the one of the outcomes of the counterterrorism and counterinsurgency use of lethally autonomous technology for military purposes operations in Iraq and Afghanistan. The asymmetrical and how the forms of subjectivity and moral agency that battlefields of these theaters, where no frontline it creates could be highly counterproductive to mission provides a buffer between combatants and civilians and effectiveness, diplomacy and conflict resolution and prevention.

This paper proposes a model for an artificial autonomous moral agent (AAMA), which is parsimonious in its ontology and minimal in its ethical assumptions. Starting from a set of moral data, this AAMA is able to learn and develop a form of moral competency. It resembles an “optimizing predictive mind,” which uses moral data (describing typical behavior of humans) and a set of dispositional traits to learn how to classify different actions (given a given background knowledge) as morally right, wrong, or neutral. When confronted with a new situation, this AAMA is supposedly able to predict a behavior consistent with the training set. This paper argues that a promising computational tool that fits our model is “neuroevolution,” i.e. evolving artificial neural networks.

Autonomization often follows after the automization on which it is based. More and more machines have to make decisions with moral implications. Machine ethics, which can be seen as an equivalent of human ethics, analyses the chances and limits of moral machines. So far, decision trees have not been commonly used for modelling moral machines. This article proposes an approach for creating annotated decision trees, and specifies their central components. The focus is on simple moral machines. The chances of such models are illustrated with the example of a self-driving car that is friendly to humans and animals. Finally the advantages and disadvantages are discussed and conclusions are drawn.

This paper describes and frames a central ethical issue–the liability problem–facing the regulation of artificial computational agents, including artificial intelligence (AI) and robotic systems, as they become increasingly autonomous, and supersede current capabilities. While it frames the issue in legal terms of liability and culpability, these terms are deeply imbued and interconnected with their ethical and moral correlate–responsibility. In order for society to benefit from advances in AI technology, it will be necessary to develop regulatory policies which manage the risk and liability of deploying systems with increasingly autonomous capabilities. However, current approaches to liability have difficulties when it comes to dealing with autonomous artificial agents because their behavior may be unpredictable to those who create and deploy them, and they will not be proper legal or moral agents. This problem is the motivation for a research project that will explore the fundamental concepts of autonomy, agency and liability; clarify the different varieties of agency that artificial systems might realize, including causal, legal and moral; and the illuminate the relationships between these. The paper will frame the problem of liability in autonomous agents, sketch out its relation to fundamental concepts in human legal and moral agency–including autonomy, agency, causation, intention, responsibility and culpability–and their applicability or inapplicability to autonomous artificial agents.

Robots collaborating with humans need to represent knowledge, reason, and learn, at the sensorimotor level and the cognitive level. This paper summarizes the capabilities of an architecture that combines the comple- mentary strengths of declarative programming, proba- bilistic graphical models, and reinforcement learning, to represent, reason with, and learn from, qualitative and quantitative descriptions of incomplete domain knowledge and uncertainty. Representation and reasoning is based on two tightly-coupled domain representations at different resolutions. For any given task, the coarse- resolution symbolic domain representation is translated to an Answer Set Prolog program, which is solved to provide a tentative plan of abstract actions, and to explain unexpected outcomes. Each abstract action is implemented by translating the relevant subset of the corresponding fine-resolution probabilistic representation to a partially observable Markov decision process (POMDP). Any high probability beliefs, obtained by the execution of actions based on the POMDP policy, update the coarse-resolution representation. When incomplete knowledge of the rules governing the domain dynamics results in plan execution not achieving the desired goal, the coarse-resolution and fine-resolution representations are used to formulate the task of incrementally and interactively discovering these rules as a reinforcement learning problem. These capabilities are illustrated in the context of a mobile robot deployed in an indoor office domain.

Robot designers commonly emphasize humanlikeness as an important design feature to make robots social or user-friendly. To understand how users make sense of the design characteristics of robots, we asked 6 participants to classify and interpret the appearance of existing robots in relation to their function and potential usefulness. All the robots had humanlike aspects in their design, and participants most commonly remarked on these humanlike features of the robots. However, the commonsense logic of the “Uncanny Valley” (UV) in HRI design, which suggests that robots should be similar to humans to some degree without being too humanlike, was not supported by participant comments, which did not correlate humanlikeness to user-friendliness in line with the UV hypothesis. Rather, participants related the design features of robots to their everyday contexts, and focused their commentary on context-dependent design implications. As a result, we suggest our understanding of the design characteristics of robots should include the perspectives of users from the earliest stages of design so we can understand their contextual interpretations of different design characteristics. Open and modularized technical platforms could support the inclusion of users in the creation of future social robots.

We present the Robot Gesture Library (RoGuE), amotion-planning approach to generating gestures. Gestures improve robot communication skills, strengthening robots as partners in a collaborative setting. Previouswork maps from environment scenario to gesture selection. This work maps from gesture selection to gesture execution. We create a flexible and common language by parameterizing gestures as task-space constraints onrobot trajectories and goals. This allows us to leverage powerful motion planners and to generalize across environments and robot morphologies. We demonstrateRoGuE on four robots: HREB, ADA, CURI and the PR2.