A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
Bayesian learning in negotiation
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
A genetic agent-based negotiation system
Computer Networks: The International Journal of Computer and Telecommunications Networking
Machine Learning
Machine Learning
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
Theoretical Comparison between the Gini Index and Information Gain Criteria
Annals of Mathematics and Artificial Intelligence
Argumentation-based negotiation
The Knowledge Engineering Review
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Learning opponents' preferences in multi-object automated negotiation
ICEC '05 Proceedings of the 7th international conference on Electronic commerce
An evolutionary learning approach for adaptive negotiation agents: Research Articles
International Journal of Intelligent Systems - Learning Approaches for Negotiation Agents and Automated Negotiation
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
An agent architecture for multi-attribute negotiation using incomplete preference information
Autonomous Agents and Multi-Agent 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
Preference Ordering in Agenda Based Multi-issue Negotiation for Service Level Agreement
WAINA '09 Proceedings of the 2009 International Conference on Advanced Information Networking and Applications Workshops
Journal of Artificial Intelligence Research
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
Preference elicitation with subjective features
Proceedings of the third ACM conference on Recommender 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
An approach to debug interactions in multi-agent system software tests
Information Sciences: an International Journal
Magentix2: A privacy-enhancing Agent Platform
Engineering Applications of Artificial Intelligence
Predicting behavior in unstructured bargaining with a probability distribution
Journal of Artificial Intelligence Research
Strategies for avoiding preference profiling in agent-based e-commerce environments
Applied Intelligence
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We consider automated negotiation as a process carried out by software agents to reach a consensus. To automate negotiation, we expect agents to understand their user's preferences, generate offers that will satisfy their user, and decide whether counter offers are satisfactory. For this purpose, a crucial aspect is the treatment of preferences. An agent not only needs to understand its own user's preferences, but also its opponent's preferences so that agreements can be reached. Accordingly, this paper proposes a learning algorithm that can be used by a producer during negotiation to understand consumer's needs and to offer services that respect consumer's 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 consumer'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, our algorithm can learn and guide the producer to create well-targeted offers. Further, our algorithm can detect whether some preferences cannot be satisfied early and thus consensus cannot be reached. Our experimental results show that the producer using our learning algorithm negotiates faster and more successfully with customers compared to several other algorithms.