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This paper describes an effort to integrate human behavior models from a range of ability, stress, emotion, decision theoretic, and motivation literatures into a game-theoretic framework. Our goal is to create a common mathematical framework (CMF) and a simulation environment that allows one to research and explore alternative behavior models to add realism to software agents – e.g., human reaction times, constrained rationality, emotive states, and cultural influences. Our CMF is based on a dynamical, game-theoretic approach to evolution and equilibria in Markov chains representing states of the world that the agents can act upon. In these worlds the agents' utilities (payoffs) are derived by a deep model of cognitive appraisal of intention achievement including assessment of emotional activation/decay relative to concern ontologies, and subject to (integrated) stress and related constraints. We present the progress to date on the mathematical framework, and on an environment for editing the various elements of the cognitive appraiser, utility generators, concern ontologies, and Markov chains. We summarize a prototype of an example training game for counter-terrorism and crowd management. Future research needs are elaborated including validity issues and the gaps in the behavioral literatures that agent developers must struggle with.
software agents, human behavior models, stress, emotion, utility
Silverman, B. G., Johns, M., Weaver, R., O'Brien, K., & Silverman, R. (2002). Human Behavior Models for Game-Theoretic Agents: Case of Crowd Tipping. Retrieved from https://repository.upenn.edu/hms/11
Date Posted: 11 July 2007
This document has been peer reviewed.