Elicitation of user preferences for multi-attribute negotiation

Agents that act on behalf of users in electronic negotiations need to elicit the required information about their users' preference structures. Based on a multi-attri\-bute utility theoretic model of user preferences, we propose an algorithm that enables an agent to learn the utility function with flexibility to accept several types of information for learning. The method combines an evolutionary learning with the application of external knowledge and local search. Empirical tests show that the algorithm provides a good learning performance.