Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

A study of safety and production problems and safety strategies associated with industrial robot systems

Imperial Users onl

Third Conference on Artificial Intelligence for Space Applications, part 1

The application of artificial intelligence to spacecraft and aerospace systems is discussed. Expert systems, robotics, space station automation, fault diagnostics, parallel processing, knowledge representation, scheduling, man-machine interfaces and neural nets are among the topics discussed

Building bridges for better machines : from machine ethics to machine explainability and back

Be it nursing robots in Japan, self-driving buses in Germany or automated hiring systems in the USA, complex artificial computing systems have become an indispensable part of our everyday lives. Two major challenges arise from this development: machine ethics and machine explainability. Machine ethics deals with behavioral constraints on systems to ensure restricted, morally acceptable behavior; machine explainability affords the means to satisfactorily explain the actions and decisions of systems so that human users can understand these systems and, thus, be assured of their socially beneficial effects. Machine ethics and explainability prove to be particularly efficient only in symbiosis. In this context, this thesis will demonstrate how machine ethics requires machine explainability and how machine explainability includes machine ethics. We develop these two facets using examples from the scenarios above. Based on these examples, we argue for a specific view of machine ethics and suggest how it can be formalized in a theoretical framework. In terms of machine explainability, we will outline how our proposed framework, by using an argumentation-based approach for decision making, can provide a foundation for machine explanations. Beyond the framework, we will also clarify the notion of machine explainability as a research area, charting its diverse and often confusing literature. To this end, we will outline what, exactly, machine explainability research aims to accomplish. Finally, we will use all these considerations as a starting point for developing evaluation criteria for good explanations, such as comprehensibility, assessability, and fidelity. Evaluating our framework using these criteria shows that it is a promising approach and augurs to outperform many other explainability approaches that have been developed so far.DFG: CRC 248: Center for Perspicuous Computing; VolkswagenStiftung: Explainable Intelligent System

L’implantation de la robotique collaborative et la gestion des ressources humaines dans le secteur manufacturier : soutenir le changement et l’adoption

Ce mémoire de maîtrise explore l’implantation de la robotique collaborative en entreprise sous l’angle des pratiques de gestion et des facteurs humains. La visée initiale de ce projet de recherche visait préalablement à circonscrire l’apport que peut prendre la gestion des ressources humaines (GRH) lors de ce type d’implantation technologique, qui implique une collaboration humain-machine plus accrue qu’auparavant. Initialement, l’objectif était donc d’identifier les pratiques de GRH à mettre en place lors de l’implantation de robots collaboratifs. Cela dit, comme ce projet de recherche présente une démarche exploratoire semi-inductive, l’objectif de recherche a évolué vers plusieurs objectifs. Cette ouverture sur de nouveaux objectifs est subséquente aux résultats obtenus lors de la revue systématique de la littérature et de la collecte de données afin de dresser un portrait plus juste, adapté à l’état des connaissances et au terrain. Les objectifs poursuivis sont les suivants : 1) identifier les pratiques de GRH et d’autres pratiques organisationnelles en matière de gestion du changement facilitant l’implantation et l’adoption des robots collaboratifs 2) identifier les facteurs associés à l’humain, au robot et à l’environnement qui influencent l’implantation des robots collaboratifs, l’adoption et la collaboration entre l’opérateur et le robot

A Widening Attack Plain

A glimpse of our digital future includes diverse actors operating on a widening attack plain with affects ranging from data disruption to death and destruction. How do we craft meaningful narratives of the future that can advise our community today? How do we combat the weaponization of data and future technology? Where do we even start? Threatcasting is a conceptual framework and process that enables multidisciplinary groups to envision and systematically plan against threats ten years in the future. In August 2016, the Army Cyber Institute convened a cross section of public, private and academic participants to model future digital threats using this process with inputs from social science, technical research, cultural history, economics, trends, expert interviews and even a little science fiction. Renowned futurist Brian David Johnson and Army Major Natalie Vanatta will explore the results of this project that not only describes tomorrow’s threats but also identifies specific actions, indicators and concrete steps that can be taken today to disrupt, mitigate and recover from these future threats.https://digitalcommons.usmalibrary.org/aci_books/1034/thumbnail.jp

Computational Theory of Mind for Human-Agent Coordination

In everyday life, people often depend on their theory of mind, i.e., their ability to reason about unobservable mental content of others to understand, explain, and predict their behaviour. Many agent-based models have been designed to develop computational theory of mind and analyze its effectiveness in various tasks and settings. However, most existing models are not generic (e.g., only applied in a given setting), not feasible (e.g., require too much information to be processed), or not human-inspired (e.g., do not capture the behavioral heuristics of humans). This hinders their applicability in many settings. Accordingly, we propose a new computational theory of mind, which captures the human decision heuristics of reasoning by abstracting individual beliefs about others. We specifically study computational affinity and show how it can be used in tandem with theory of mind reasoning when designing agent models for human-agent negotiation. We perform two-agent simulations to analyze the role of affinity in getting to agreements when there is a bound on the time to be spent for negotiating. Our results suggest that modeling affinity can ease the negotiation process by decreasing the number of rounds needed for an agreement as well as yield a higher benefit for agents with theory of mind reasoning.</p