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Abstract : |
In this paper, we argue that partially adversarial and partially cooperative (PARC) problems in distributed artificial intelligence can be mapped into a formalism called distributed constraint optimization problems (DCOPs), which generalize distributed constraint satisfaction problems [Yokoo, et al. 90] by introducing weak constraints (preferences). We discuss several solution criteria for DCOP and clarify the relation between these criteria and different levels of agent rationality [Rosenschein and Genesereth 85], and show the algorithms for solving DCOPs in which agents incrementally exchange only necessary information to converge on a mutually satisfiable solution. 1, |
