We advocate a case-supported principle-based behavior paradigm coupled with the Fractal robot architecture as a means to control an eldercare robot. The most ethically preferable action at any given moment is determined using a principle, abstracted from cases where a consensus of ethicists exists.

As robots evolve into an integral part of the human ecosystem, humans and robots will be involved in a multitude of collaborative tasks that require complex coordination and cooperation. Indeed there has been extensive work in the robotics, planning as well as the human-robot interaction communities to understand and facilitate such seamless teaming. However, it has been argued that their increased participation as independent autonomous agents in hitherto human-habited environments has introduced many new challenges to the view of traditional human-robot teaming. When robots are deployed with independent and often self-sufficient tasks in a shared workspace, teams are often not formed explicitly and multiple teams cohabiting an environment interact more like colleagues rather than teammates. In this paper, we formalize these differences and analyze metrics to characterize autonomous behavior in such human-robot cohabitation settings.

In many situations, agents are required to use a set of strategies (behaviors) and switch among them during the course of an interaction. This work focuses on the problem of recognizing the strategy used by an agent within a small number of interactions. We propose using a Bayesian framework to address this problem. Bayesian policy reuse (BPR) has been empirically shown to be efficient at correctly detecting the best policy to use from a library in sequential decision tasks. In this paper we extend BPR to adversarial settings, in particular, to opponents that switch from one stationary strategy to another. Our proposed extension enables learning new models in an online fashion when the learning agent detects that the current policies are not performing optimally. Experiments presented in repeated games show that our approach is capable of efficiently detecting opponent strategies and reacting quickly to behavior switches, thereby yielding better performance than state-of-the-art approaches in terms of average rewards.

Human-machine systems have expanded in terms of their sensing, communication, and computational capabilities. These capabilities have led to developments of a variety of sensor systems, like robotic platforms. There are benefits to these new sensor systems, however, these benefits have been offset by new difficulties; dynamic data overload, keeping pace with changing tempo, and managing data flows from multiple sensors feeds. One approach to manage data overload from multiple sensor feeds are computational models of attention. These models also address an important aspect of human-machine symbiosis, the need for machines agents to understand attention, manage interaction based on the flow of attention, and anticipate the flow of attention in the future. Unfortunately, existing computational models of attention use assumptions that limit their applicability to human-machine systems. The Artificial Attention Architecture is introduced and demonstrates how computational models of attention can be extended to handle multi-agent, multi-sensor systems. The Artificial Attention Architecture addresses important properties of human-machine systems like the need to build symbiosis between people searching for meaning in extensive data flows and the computational algorithms processing complex and dynamic data flows.

The word symbiosis (Merriam-Webster 2015) has its origins in biology where it means “the relationship between two different kinds of living things that live together and depend on each other.” When referring to symbiotic cognitive computing, we expand this definition to include both people and intelligent computational agents who work together in a partnership (Farrell et. al 2005). Cognitive objects embody these intelligent agents, providing a physical object that may sense, compute, react, and interact with the power of cognitive computing. In this paper, we describe a few preliminary design explorations that investigate the impact of being surrounded by cognitive objects during group meetings. We frame a research agenda around the construction, programming, and usage of cognitive objects in work and home environments, and for use cases across industries such as oil and gas, healthcare and agriculture.

We present a new game-theoretic framework where Bayesian players engage in a Markov game and each has private but imperfect information regarding other players' types. Instead of utilizing Harsanyi's abstract types and a common prior distribution, we construct player types whose structure is explicit and induces a finite level belief hierarchy. We characterize equilibria in this game and formalize the computation of finding such equilibria as a constraint satisfaction problem. The effectiveness of the new framework is demonstrated on two ad hoc team work domains.

As robots evolve into fully autonomous agents, settings involving human-robot teams will evolve into human-robot societies, where multiple independent agents and teams, both humans and robots, coexist and work in harmony. Given such a scenario, the question we ask is - How can two or more such agents dynamically form coalitions or teams for mutual benefit with minimal prior coordination? In this work, we provide a game theoretic solution to address this problem. We will first look at a situation with full information, provide approximations to compute the extensive form game more efficiently, and then extend the formulation to account for scenarios when the human is not totally confident of its potential partner's intentions. Finally we will look at possible extensions of the game, that can capture different aspects of decision making with respect to ad-hoc coalition formation in human-robot societies.

A very recent algorithm shows search of games of imperfect information has been around how both theoretical problems can be fixed (Lisý, Lanctot, for many years. The approach is appealing, for a number of and Bowling 2015), but has yet to be applied to large games reasons: it allows the usage of well-known methods from typically used for search. More recently overestimation of perfect information games, its complexity is magnitudes MAX's knowledge is also dealt with in the field of general lower than the problem of weakly solving a game in the game play (Schofield, Cerexhe, and Thielscher 2013). To the sense of game theory, it can be used in a justin-time manner best of our knowledge, all literature on the deficiencies of (no precalculation phase needed) even for games with PIMC concentrates on the overestimation of MAX's knowledge.

The The valuation of a given security policy is often predicated varying extent to which a compromised account at one service upon assumptions that fail in practice (e.g, (Blythe, Koppel, can escalate to compromise accounts on other services and Smith 2013)). For example, a plethora of password further complicates matters. And we're just scratching the discussions begin with the password paradox: users must surface. In such complex environments, a mathematical pick strong passwords-so strong that the average user cannot analysis of security can quickly become unwieldy, while a remember them-yet they must never be written down.

Much existing work examining the ethical behaviors of robots does not consider the impact and effects of long- term human-robot interactions. A robot teammate, col- laborator or helper is often expected to increase task performance, individually or of the team, but little dis- cussion is usually devoted to how such a robot should balance the task requirements with building and main- taining a “working relationship” with a human partner, much less appropriate social relations outside that team. We propose the “Relational Enhancement” framework for the design and evaluation of long-term interactions, which composed of interrelated concepts of efficiency, solidarity, and prosocial concern. We discuss how this framework can be used to evaluate common existing ap- proaches in cognitive architectures for robots and then examine how social norms and mental simulation may contribute to each of the components of the framework.