A Similarity Evaluation Technique for Cooperative Problem Solving with a Group of Agents
Evaluation of distance or similarity is very important in cooperative problem solving with a group of agents. Distance between problems is used by agents to recognize nearest solved problems for a new problem, distance between solutions is necessary to compare and evaluate the solutions made by different agents, and distance between agents is useful to evaluate weights of the agents to be able to integrate them by weighted voting. The goal of this paper is to develop a similarity evaluation technique to be used for cooperative problem solving with a group of agents. Virtual training environment used for this goal is represented by predicates that define relationships within three sets: problems, solutions, and agents. We derive and interpret both internal and external relations between the pairs of subsets taken of the three sets: problems, solutions, and agents. The refinement technique presented is based on the derivation of the most supported solution of the group of agents and refining it further using a multilevel structure of agents.
KeywordsWeighted Vote External Relation Multilevel Structure Total Support Cooperative Problem
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