Learning Experiments in a Heterogeneous Multi-agent System
نویسندگان
چکیده
Self organization for e cient distributed search control has received much attention previously but the work presented in this paper repre sents one of the few attempts at demonstrating its viability and utility in an agent based sys tem involving complex interactions within the agent set We discuss experiments with a het erogeneous multi agent parametric design sys tem called L TEAM where machine learning techniques endow the agents with capabilities to learn their organizational roles in negotiated search and to learn meta level knowledge about the composite search spaces We tested the sys tem on a steam condenser design domain and empirically demonstrated its usefulness
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