Deploying Affect-Inspired Mechanisms to Enhance Agent Decision-Making and Communication

Abstract

Computer agents are required to make appropriate decisions quickly and efficiently. As the environments in which they act become increasingly complex, efficient decision-making becomes significantly more challenging. This thesis examines the positive ways in which human emotions influence people’s ability to make good decisions in complex, uncertain contexts, and develops computational analogues of these beneficial functions, demonstrating their usefulness in agent decision-making and communication. For decision-making by a single agent in large-scale environments with stochasticity and high uncertainty, the thesis presents GRUE (Goal Re-prioritization Using Emotion), a decision-making technique that deploys emotion-inspired computational operators to dynamically re-prioritize the agent’s goals. In two complex domains, GRUE is shown to result in improved agent performance over many existing techniques. Agents working in groups benefit from communicating and sharing information that would otherwise be unobservable. The thesis defines an affective signaling mechanism, inspired by the beneficial communicative functions of human emotion, that increases coordination. In two studies, agents using the mechanism are shown to make faster and more accurate inferences than agents that do not signal, resulting in improved performance. Moreover, affective signals confer performance increases equivalent to those achieved by broadcasting agents’ entire private state information. Emotions are also useful signals in agents’ interactions with people, influencing people’s perceptions of them. A computer-human negotiation study is presented, in which virtual agents expressed emotion. Agents whose emotion expressions matched their negotiation strategy were perceived as more trustworthy, and they were more likely to be selected for future interactions. In addition, to address similar limitations in strategic environments, this thesis uses the theory of reasoning patters in complex game-theoretic settings. An algorithm is presented that speeds up equilibrium computation in certain classes of games. For Bayesian games, with and without a common prior, the thesis also discusses a novel graphical formalism that allows agents’ possibly inconsistent beliefs to be succinctly represented, and for reasoning patterns to be defined in such games. Finally, the thesis presents a technique for generating advice from a game’s reasoning patterns for human decision-makers, and demonstrates empirically that such advice helps people make better decisions in a complex game.