Machine Learning
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Learning consumer preferences using semantic similarity
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Opponent modelling in automated multi-issue negotiation using Bayesian learning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Modelling user preferences and mediating agents in electronic commerce
Knowledge-Based Systems
A fast method for learning non-linear preferences online using anonymous negotiation data
TADA/AMEC'06 Proceedings of the 2006 AAMAS workshop and TADA/AMEC 2006 conference on Agent-mediated electronic commerce: automated negotiation and strategy design for electronic markets
Learning opponent's preferences for effective negotiation: an approach based on concept learning
Autonomous Agents and Multi-Agent Systems
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Successful negotiation depends on understanding and responding to participants' needs. Many negotiation approaches assume identical needs and do not take into account other preferences of the participants. However, preferences play a crucial role in the outcome of negotiations. Accordingly, we propose a learning algorithm that can be used by a producer during negotiation to understand consumer's needs and to offer services that respect these preferences. Our proposed algorithm is based on inductive learning but also incorporates the idea of revision. Thus, as the negotiation proceeds, a producer can revise its idea of the customer's preferences. The learning is enhanced with the use of ontologies so that similar service requests can be identified and treated similarly. Further, the algorithm is targeted to learning both conjunctive as well as disjunctive preferences. Hence, even if the consumer's preferences are specified in complex ways, such as conditional rules, our algorithm can learn and guide the producer to create well-targeted offers.