Engineering Machine Learning Techniques into Multi-Agent Systems
نویسندگان
چکیده
Agent technology is a Distributed Artificial Intelligence (DAI) approach to implement autonomous entities driven by beliefs, goals, capabilities, plans, and agency properties: adaptation, interaction, learning, etc. Software agents are the focus of considerable research by the artificial intelligence community, but there is still much to be done in the field of software engineering in order to systematically create large scale multi-agent systems. In this paper, we present an object-oriented framework for building a distributed multi-agent system. We explore in this framework the intersection of DAI with machine learning techniques, and propose a methodology for introducing intelligence in a multi-agent system. A multi-agent system created for the Trading Agent Competition is presented as a case study. Our engineering approach provides a performance gain of 97,3% due to the introduction of machine learning techniques.
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