A Model of Trust, Moods, and Emotions in Multiagent Systems and its Empirical Evaluation

Abstract We study the interplay of moods, emotions, and trust in decisionmaking contexts characterized by commitments among agents. We develop a general approach representing the relationships among these concepts via a Bayesian network model. Our approach incorporates insights from the literature and provides a computational methodology for identifying improved Bayesian models. Based on observations from an empirical study, we motivate a refined Bayesian model involving the above-mentioned concepts that goes beyond the relationships known in the literature. Our findings include (1) the violation of a commitment affects trust more than its satisfaction; (2) goal satisfaction affects mood and emotion more than commitment satisfaction, but the outcome of a commitment affects trust more than the outcome of a goal; and (3) an agent's prior mood and trust affect whether it satisfies its commitments

Robotic Faces: Exploring Dynamical Patterns of Social Interaction between Humans and Robots

Thesis (Ph.D.) - Indiana University, Informatics, 2015The purpose of this dissertation is two-fold: 1) to develop an empirically-based design for an interactive robotic face, and 2) to understand how dynamical aspects of social interaction may be leveraged to design better interactive technologies and/or further our understanding of social cognition. Understanding the role that dynamics plays in social cognition is a challenging problem. This is particularly true in studying cognition via human-robot interaction, which entails both the natural social cognition of the human and the “artificial intelligence” of the robot. Clearly, humans who are interacting with other humans (or even other mammals such as dogs) are cognizant of the social nature of the interaction – their behavior in those cases differs from that when interacting with inanimate objects such as tools. Humans (and many other animals) have some awareness of “social”, some sense of other agents. However, it is not clear how or why. Social interaction patterns vary across culture, context, and individual characteristics of the human interactor. These factors are subsumed into the larger interaction system, influencing the unfolding of the system over time (i.e. the dynamics). The overarching question is whether we can figure out how to utilize factors that influence the dynamics of the social interaction in order to imbue our interactive technologies (robots, clinical AI, decision support systems, etc.) with some "awareness of social", and potentially create more natural interaction paradigms for those technologies. In this work, we explore the above questions across a range of studies, including lab-based experiments, field observations, and placing autonomous, interactive robotic faces in public spaces. We also discuss future work, how this research relates to making sense of what a robot "sees", creating data-driven models of robot social behavior, and development of robotic face personalities

Project knole: an autocosmic approach to authoring resonant computational characters

Project knole, consisting of this thesis and a mixed reality installation artwork centred around a computational simulation, is a practice-based response to the question of how a character in a work of computational narrative art might maintain their defining quality of dynamic agency within a system (arguably one of the key potentials of the form), while achieving the ‘resonant’ qualities of characters in more materially-static artforms. In all aspects of this project, I explore a new design philosophy for achieving this balance; between the authorship of a procedural computational system, and the ability of that system to ‘resonate’ with the imagination of an audience. This philosophy, which I term the ‘autocosmic’, seeks inspiration for the curation of audience response outside the obvious boundaries of artistic discipline, across the wider spectrum of human imaginative engagement; examples often drawn from mostly non-aesthetic domains. As well as defining the terms ‘resonance’ and ‘autocosmic’, and delineating my methodology more generally, this thesis demonstrates how the ‘autocosmic’ was employed within my creative work. In particular, it shows how some of the perennial problems of computational character development might be mediated by exploring other non-aesthetic examples of imaginative, narrative engagement with personified systems. In the context of this project, such examples come from the historio-cultural relationship between human beings and the environments they inhabit, outside of formal artistic practice. From this ‘autocosmic’ launchpad, I have developed an artwork that starts to explore how this rich cultural and biological lineage of human social engagement with systemic place can be applied fruitfully to the development of a ‘resonant’ computational character

Emotions as durative dynamic state for action selection

This paper presents a representation system for maintaining interacting durative states to replicate realistic emotional control. Our model, the Dynamic Emotion Representation (DER) integrates emotional responses and keeps track of emotion intensities changing over time. The developer can specify an interacting network of emotional states with appropriate onsets, sustains and decays. The levels of these states can be used as input for action selection, including emotional expression. We present both a general representational framework and a specific instance of a DER network constructed for a virtual character. The character’s DER uses three types of emotional state as classified by duration timescales, in keeping with current emotional theory. The system is demonstrated with a virtual actor.