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.

Wizard-of-Oz (WoZ) is a common technique enabling HRI researchers to explore aspects of interaction not yet backed by autonomous systems. A standardized, open, and flexible WoZ framework could therefore serve the community and accelerate research both for the design of robotic systems and for their evaluation. This paper presents the definition of OpenWoZ , a Wizard-of-Oz framework for HRI, designed to be updated during operation by the researcher controlling the robot. OpenWoZ is implemented as a thin HTTP server running on the robot, and a cloud-backed multi-platform client schema. The WoZ server accepts representational state transfer (REST) requests from a number and variety of clients simultaneously. This "separation of concerns" in OpenWoZ allows addition of commands, new sequencing of behaviors, and adjustment of parameters, all during run-time.

Extended school absence during K-12 education can have anegative impact on both the educational and social development of a child. Mobile Remote Presence (MRP) can helpenable continued access to K-12 education for children withhealth challenges. However, most MRP platforms are targetedtowards adult users in domains such as the workplace.The importance of social interaction and engagement in K-12 education creates a unique set of needs and challenges foran MRP platform. In this work, we discuss the benefits ofMRP usage for K-12 education, ongoing challenges for MRPacross domains, and the requirements of an MRP platform forthe classroom.

We consider the effects of a human in the loop with respect to dramatic behavior in a team environment in this position paper. This dramatic behavior is captured mathematically as a jump in behavior. We cite recent examples and discuss earlier work in the cognitive sciences. We consider the problem in light of network science.

It is nowadays our responsibility to convince our contemporary citizens that AI devices as UGVs (Unmanned Ground Vehicles) and UAVs (Unmanned Aerial Vehicles) are crucial actors of today’s life in a dual domains, both civilian and military. In particular, the decision process is the main component of every military operation and is of high interest because of two main reasons : it is necessary designed to cope with conflict issues and it requires a very complex planning process to be successful. The difficulty to find a good plan is worse in urban areas because of the high uncertainty due to the topology of these areas, the presence of civilians, who can be hostile or friendly, and the unpredictable nature of enemies. The idea in that paper is to qualify what can be a valid computed plan in that context , i.e. welldesigned for recovering of peace, rescue operations after a bombing event, hostage salvage, non-combatant evacuation operations, civil-military co-operation, ...., in urban areas. This planning process leads to associate actually four components, the representation of the tactical scheme, the implementation of the tactical scheme as the behaviour of special forces, military units or emergency squads, the proof process or the explanation process, and finally the handling of external factors depending on the current environment or the current context in which the operation takes place. This paper uses a quaternary representation called the epistemological quadriptych, in order to highlight that the integration of UGVs or UAVs devices requires actually to understand the role of knowledge and behaviour and to provide secure and valid action plans, i.e. which can be explained and justified.

However, foundational problems remain in the either mindfully or inadvertently by individuals or teams of continuing development of AI for team autonomy, humans. One worry about this bright future is that jobs especially with objective measures able to optimize team may be lost; from Mims (2015), function, performance and composition. Something potentially momentous is happening inside AI approaches often attempt to address autonomy by startups, and it's a practice that many of their established modeling aspects of human decision-making or behavior.

Public goods, like food sharing and social health systems, may prosper when prior agreements to contribute are feasible and all participants commit to do so. Yet, free-riders may exploit such agreements, requiring then committers to decide whether to enact the public good when others do not commit. So deciding removes all benefits from free-riders but also from those who are willing to establish the beneficial resource. Here we discuss our work wherein we show, within the framework of the one-shot Public Goods Game (PGG) and using methods of Evolutionary Game Theory (EGT), that (i) implementing extra measures, delimiting benefits to free-riders, often leads to more favorable societal outcomes, especially in larger groups and highly beneficial public goods situations, even if so doing is costlier, and (ii) when restriction mechanism is not available, participation level (i.e. how many other players commit to the PGG cooperation) plays a crucial role in the decision making of commitment proposers, for their survival as well as for promoting the emergence of cooperation. Hence, there exist ethical fine tunings to be observed whenever establishing PGGs, be they for humans or non-humans, for otherwise the supporting joint moral ground may escape from under everyone’s feet.