Applications of robotic and wearable sensors based systems for human assistance or health monitoring have been gaining popularity in recent years. Among its diverse applications, therapeutic robotic systems have been utilized in the muscular physiotherapies for movement training, wrist and arm treatment for injuries and overexertions, and other therapies. Applications of wearable sensors for human assistance or health monitoring have been also gaining popularity in recent years. Wireless wearable sensor systems enable proactive personal health management and the ubiquitous monitoring of vital signs to keep an active watch on immediate health conditions. In this paper, we develop a system that consists of multiple wearable sensors, software agents and robots, where a robot has the intelligence to process its own observed data, the collected wearable sensor data, and to aggregate the information into a single compiled report. Our system is also able to detect severe abnormalities with the well-being of the monitored individual as detected by the sensors and to create immediate alerts. Our preliminary experimental results show that our system is accurate in detecting and monitoring basic human conditions. We posit that the approach of non-invasive monitoring, when combined with an alert system, will make this a desirable personalized well-being monitoring system in future health care.

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.

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.

Team decision problems are one of the fundamental problems in decentralized decision making. Because team decision problems are NP-hard, it is important to find methods for reducing their complexity. Wu and Lall gave a definition of sufficient statisticsfor team decision problems, and demonstrated that these statistics are sufficient for optimality, and possess other desirable properties such as being readily updated when additional information becomes available. More recently, Lemon and Lall defined weak sufficient statistics for team decision problems, and showed that these statistics are sufficient for optimality and necessary for simultaneous optimality with respect to all cost functions. This prior work studied the extent to which the complexity of team decision problems can be reduced while maintaining exact optimality. However,when faced with a computationally difficult problem, we are often willing to sacrifice exact optimality for significant reductions in complexity. In this paper we define approximate sufficient statistics, which are a generalization of weak team sufficient statistics. Then we prove that these statistics are quantifiably close to being optimal.

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.

We discuss philosophical and ethical issues that arise from a dialogue system intended to portray a real person, using recordings of the person together with a machine agent that selects recordings during a synchronous conversation with a user. System output may count as actions of the speaker if the speaker intends to communicate with users and the outputs represent what the speaker would have chosen to say in context; in such cases the system can justifiably be said to be holding a conversation that is offset in time. The autonomous agent may at times misrepresent the speaker's intentions, and such failures are analogous to good-faith misunderstandings. The user may or may not need to be informed that the speaker is not organically present, depending on the application.

Based on the development of several different HRI scenarios using different robots, we have been establishing the SERA ecosystem. SERA is composed of both a model and tools for integrating an AI agent with a robotic embodiment, in humanrobot interaction scenarios. We present the model, and several of the reusable tools that were developed, namely Thalamus, Skene and Nutty Tracks. Finally we exemplify how such tools and model have been used and integrated in five different HRI scenarios using the NAO, Keepon and EMYS robots. Figure 1: Our methodology as an intersection of CGI animation, Human-robot interaction (HRI) systems are spreading as a IVA and robotics techniques.

When designing a robot to interact with people, the decision to incorporate a robot arm may arise. In this paper, we investigate adding an inexpensive, functional arm to our mobile CoBot service robots. Specifically, we examine two-dimensional extendable pantograph arms for CoBot. Pantograph arms have intuitive kinematics and inverse kinematics. Pantograph arms are modular and adding additional linkages corresponds to simple changes in the kinematic calculations. These arms have several advantages (and disadvantages) compared to traditional robot arms. A prototype pantograph arm is currently in development and our goal is to attach a modular pantograph arm to CoBot to perform simple needed tasks, such as knocking on doors and pressing elevator buttons.

There are many successful methods for transferring information from one agent to another. One approach, taken in this work, is to have one (source) agent demonstrate a policy to a second (target) agent, and then have that second agent improve upon the policy. By allowing the target agent to observe the source agent's demonstrations, rather than relying on other types of direct knowledge transfer like Q-values, rules, or shared representations, we remove the need for the agents to know anything about each other's internal representation or have a shared language. In this work, we introduce a refinement to HAT, an existing transfer learning method, by integrating the target agent's confidence in its representation of the source agent's policy. Results show that a target agent can effectively 1) improve its initial performance relative to learning without transfer (jumpstart) and 2) improve its performance relative to the source agent (total reward). Furthermore, both the jumpstart and total reward are improved with this new refinement, relative to learning without transfer and relative to learning with HAT.