A multi-expert FreeCiv agent that used reinforcement learning to pick among conflicting human rules beat every single-expert rule set and won in 287 average turns versus 291 for the best expert.
Intelligent Techniques for Resolving Conflicts of Knowledge in Multi-Agent Decision Support Systems
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abstract
This paper focuses on some of the key intelligent techniques for conflict resolution in Multi-Agent Decision Support Systems.
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Playing a Strategy Game with Knowledge-Based Reinforcement Learning
A multi-expert FreeCiv agent that used reinforcement learning to pick among conflicting human rules beat every single-expert rule set and won in 287 average turns versus 291 for the best expert.