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Author Prasad, M. V. Nagendra ♦ Lesser, Victor R.
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Partial Local View ♦ Learning System ♦ Coordination Problem Instance ♦ Abstract Characterization ♦ Situation-specific Learning ♦ Dynamic Choice ♦ Cooperative Multi-agent System ♦ Cooperative Control Knowledge ♦ Coordination Strategy ♦ Complex Multi-agent System ♦ Appropriate Coordination Strategy ♦ Control Skill ♦ Meta-level Information ♦ Cooperative Agent ♦ Control Uncertainty
Description The work presented here deals with ways to improve problem solving control skills of cooperative agents. We propose situation-specific learning as a way to learn cooperative control knowledge in complex multi-agent systems. We demonstrate the power of situation-specific learning in the context of dynamic choice of a coordination strategy based on the problem solving situation. We present a learning system, called COLLAGE, that uses meta-level information in the form of abstract characterization of the coordination problem instance to learn to choose the appropriate coordination strategy from among a class of strategies. The work presented here deals with ways to improve problem solving control skills of cooperative agents. Problem solving control is the process by which an agent organizes its computation. An agent in a MAS faces control uncertainties due to its partial local view of the problem solving states of the other agents in the system. Uncertainties about progress of problem s...
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 1997-01-01
Publisher Institution In Workshop on Multi-Agent Learning, AAAI-97