Learning disjunctive preferences for negotiating effectively

ABSTRACT

Successful negotiation depends on understanding and responding to participants' needs. Many negotiation approaches assume identical needs (e.g., minimizing costs) 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 negotiation framework where producer agents learn the preferences of consumer preferences over time and negotiates based on this new knowledge. Our proposed approach 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. This enables us to learn conjunctive as well as disjunctive preferences. Even if the consumer's preferences are specified in complex ways, such as conditional rules, our approach can learn and guide the producer to create well-targeted offers. Our experimental work shows that our proposed approach completes negotiation faster than similar approaches, especially if the producer will not be able to satisfy consumer's requests properly.

REFERENCES

• 1: Reyhan Aydoğan , Pinar Yolum, Learning consumer preferences using semantic similarity, Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems, May 14-18, 2007, Honolulu, Hawaii [doi>10.1145/1329125.1329401]
• 2: Thomas M. Mitchell, Machine Learning, McGraw-Hill, Inc., New York, NY, 1997

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence

General Terms:
Algorithms, Experimentation

Keywords:
inductive learning, negotiation, ontology, semantic similarity