RI: Small: Understanding Value-based Multiagent Learning and Its Applications

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This project explores the behavior of value-based learning methods in multi-agent environments. Value-based methods make decisions by using experience to estimate the utility impact of alternatives and choosing those with high predicted value. Because they evaluate components of behavior instead of treating behaviors as atomic units, they are computationally and statistically efficient. While these methods have been used in computational experiments for many years, only recently have researchers begun to formally characterize their behavior. Our own preliminary work is finding that some value-based methods exhibit super-Nash behavior, making them particularly worthy of study.More specifically, we are analyzing, mathematically and experimentally, how value-based algorithms perform in several classes of simulated games of varying complexity from the artificial intelligence community, multi-agent engineering applications drawn from the wireless networking area, and as models of human and animal decision making in collaboration with cognitive neuroscientists. Where possible, we are refining existing value-based algorithms to work more efficiently, robustly, and generally than existing algorithms. We are also designing educational outreach activities, including creating entertaining instructional videos on how to promote cooperative behavior in real-life social dilemmas.