Reasoning and Negotiating with Complex Preferences Using CP-Nets
- 344 Downloads
Automated negotiation is important for carrying out flexible transactions. Agents that take part in automated negotiation need to have a concise representation of their user’s preferences and should be able to reason on these preferences effectively. We develop an automated negotiation platform wherein consumer agents negotiate with producer agents about services. A consumer agent represents its user’s preferences in a compact way using a CP-net, which is a structure that allows users to order their preferences based on the different value combinations of attributes. Acquiring user’s preferences in a compact way is crucial since it significantly decreases the number of questions to be asked to the user by the consumer agent. We design strategies for consumer agents to reason on and negotiate effectively with the preference graph induced from a CP-net. These strategies are designed to generate deals that are acceptable by the provider and the consumer. We compare our proposed strategies in terms of how well and how quickly they can find desirable deals for the consumer.
KeywordsChild Node White Wine Service Node Preference Graph Automate Negotiation
Unable to display preview. Download preview PDF.
- 2.Gonzales, C., Perny, P.: GAI networks for utility elicitation. In: KR 2004 (2004)Google Scholar
- 3.Boutilier, C., Brafman, R.I., Domshlak, C., Hoos, H.H., Poole, D.: CP-nets: A Tool for Representing and Reasoning with Conditional Ceteris Paribus Preference Statements. Journal of Artificial Intelligence Research (JAIR), 135–191 (2004)Google Scholar
- 4.Aydoğan, R., Yolum, P.: Learning Consumer Preferences Using Semantic Similarity. In: 6th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), Hawaii, USA, May 2007, pp. 1293–1300 (2007)Google Scholar
- 7.Wine Ontology (2003), http://www.w3.org/TR/2003/CR-owl-guide-20030818/wine.rdf
- 8.OWL Web Ontology Language Guide (2004), http://www.w3.org/TR/owl-guide
- 9.Jena (2006), http://jena.sourceforge.net/
- 10.Tykhonov, D., Hindriks, K.: Opponent Modelling in Automated Multi-Issue Negotiation Using Bayesian Learning. In: 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), Estorial, Portugal (May 2008)Google Scholar