Abstract
AbstractThis paper presents an evolutionary negotiating agent to conduct negotiation tasks between power generating and consuming companies in electricity markets. The agent select the best negotiation strategy that meets the underlying company objectives and interests. It generates a sequence of improving strategy population as the outcome of a search modeled by the selection, crossover, and mutation genetic operators. Agents use a content specification language based on an extended object model to specify the requirements, constraints, and negotiation strategic rules, which are used by the negotiation server to conduct a negotiation. A design architecture for negotiation is presented with KQML communication primitives. Various software technologies have been used for implementation and tested in a C++ environment.KeywordsNegotiation ProcessElectricity MarketPrice IssueBidding StrategyNegotiation StrategyThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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