Learning to trust in the competence and commitment of agents |
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Authors: | Michael J Smith Marie desJardins |
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Affiliation: | (1) University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA |
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Abstract: | For agents to collaborate in open multi-agent systems, each agent must trust in the other agents’ ability to complete tasks
and willingness to cooperate. Agents need to decide between cooperative and opportunistic behavior based on their assessment
of another agents’ trustworthiness. In particular, an agent can have two beliefs about a potential partner that tend to indicate
trustworthiness: that the partner is competent and that the partner expects to engage in future interactions. This paper explores an approach that models competence as an agent’s probability of successfully performing an action, and
models belief in future interactions as a discount factor. We evaluate the underlying decision framework’s performance given
accurate knowledge of the model’s parameters in an evolutionary game setting. We then introduce a game-theoretic framework
in which an agent can learn a model of another agent online, using the Harsanyi transformation. The learning agents evaluate
a set of competing hypotheses about another agent during the simulated play of an indefinitely repeated game. The Harsanyi
strategy is shown to demonstrate robust and successful online play against a variety of static, classic, and learning strategies
in a variable-payoff Iterated Prisoner’s Dilemma setting. |
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Keywords: | Trust Reputation Learning Game theory Decision theory Bayesian updating Q-learning Harsanyi transformation Prisoner’ s dilemma IPD Evolutionary games Pavlov Bayesian Model Averaging |
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