Pragmatic Multi-Agent Learning

Andrew Garland

Early models of procedural learning assumed actors were isolated, model-based thinkers. More recently, learning techniques have become more sophisticated as this assumption has been replaced with more realistic ones. To date, however, there has been no thorough investigation of multiple, heterogeneous, situated agents who learn from the pragmatics of their domain rather than from a model. This research focuses on this important problem and develops learning techniques that allow agents to improve their performance in a dynamic environment by learning from past run-time behavior.

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