Communications of the ACM
Machine Learning
Value-Oriented Electronic Commerce
IEEE Internet Computing
Splitting and Merging Version Spaces to Learn Disjunctive Concepts
IEEE Transactions on Knowledge and Data Engineering
Incremental Induction of Decision Trees
Machine Learning
Machine Learning
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Dialogues for Negotiation: Agent Varieties and Dialogue Sequences
ATAL '01 Revised Papers from the 8th International Workshop on Intelligent Agents VIII
Optimal agendas for multi-issue negotiation
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Foundations of Soft Case-Based Reasoning
Foundations of Soft Case-Based Reasoning
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
On possibilistic case-based reasoning for selecting partners for multi-attribute agent negotiation
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
A note on the utility of incremental learning
AI Communications
Ontologies for supporting negotiation in e-commerce
Engineering Applications of Artificial Intelligence
Content-oriented composite service negotiation with complex preferences
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: doctoral mentoring program
Learning disjunctive preferences for negotiating effectively
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Ontology-Based Learning for Negotiation
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Learning opponent's preferences for effective negotiation: an approach based on concept learning
Autonomous Agents and Multi-Agent Systems
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In online, dynamic environments, the services requested by consumers may not be readily served by the providers. This requires the service consumers and providers to negotiate their service needs and offers. Multiagent negotiation approaches typically assume that the parties agree on service content and focus on finding a consensus on service price. In contrast, this work develops an approach through which the parties can negotiate the content of a service. This calls for a negotiation approach in which the parties can understand the semantics of their requests and offers and learn each other's preferences incrementally over time. Accordingly, we propose an architecture in which both consumers and producers use a shared ontology to negotiate a service. Through repetitive interactions, the provider learns consumers' needs accurately and can make better targeted offers. To enable fast and accurate learning of preferences, we develop an extension to Version Space and compare it with existing learning techniques. We further develop a metric for measuring semantic similarity between services and compare the performance of our approach using different similarity metrics.