Knowledge engineering and management: the CommonKADS methodology
Knowledge engineering and management: the CommonKADS methodology
Modeling user preferences via theory refinement
Proceedings of the 6th international conference on Intelligent user interfaces
Some Experiments on Learning Soft Constraints
CP '98 Proceedings of the 4th International Conference on Principles and Practice of Constraint Programming
Effectiveness of Preference Elicitation in Combinatorial Auctions
AAMAS '02 Revised Papers from the Workshop on Agent Mediated Electronic Commerce on Agent-Mediated Electronic Commerce IV, Designing Mechanisms and Systems
Similarity Measures on Preference Structures, Part II: Utility Functions
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Visual exploration and incremental utility elicitation
Eighteenth national conference on Artificial intelligence
Similarity of personal preferences: theoretical foundations and empirical analysis
Artificial Intelligence
On Agent-Mediated Electronic Commerce
IEEE Transactions on Knowledge and Data Engineering
Preference elicitation via theory refinement
The Journal of Machine Learning Research
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
Acquiring domain knowledge for negotiating agents: a case of study
International Journal of Human-Computer Studies
Learning user preferences for multi-attribute negotiation: an evolutionary approach
CEEMAS'03 Proceedings of the 3rd Central and Eastern European conference on Multi-agent systems
Toward case-based preference elicitation: similarity measures on preference structures
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Cooperative negotiation in autonomic systems using incremental utility elicitation
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Acquiring domain knowledge for negotiating agents: a case of study
International Journal of Human-Computer Studies
A fuzzy logic based method to acquire user threshold of minimum-support for mining association rules
Information Sciences—Informatics and Computer Science: An International Journal
ICEC '04 Proceedings of the 6th international conference on Electronic commerce
An Architecture for Flexible Web Service QoS Negotiation
EDOC '05 Proceedings of the Ninth IEEE International EDOC Enterprise Computing Conference
International Journal of Human-Computer Studies
A spectrum of compromise aggregation operators for multi-attribute decision making
Artificial Intelligence
Exploiting Preferences for Minimal Credential Disclosure in Policy-Driven Trust Negotiations
SDM '08 Proceedings of the 5th VLDB workshop on Secure Data Management
Computer Networks: The International Journal of Computer and Telecommunications Networking
Agent-based assistant for e-negotiations
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
KEMNAD: A Knowledge Engineering Methodology For Negotiating Agent Development
Computational Intelligence
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A wide range of algorithms have been developed for various types of automated negotiation. In developing such algorithms the main focus has been on their efficiency and their effectiveness. However, this is only part of the picture. Agents typically negotiate on behalf of their owners and for this to be effective the agent must be able to adequately represent the owners' preferences. However, the process by which such knowledge is acquired is typically left unspecified. To remove this shortcoming, we present a case study indicating how the knowledge for a particular negotiation algorithm can be acquired. More precisely, according to the analysis on the automated negotiation model, we identified that user trade-off preferences play a fundamental role in negotiation in general. This topic has been addressed little in the research area of user preference elicitation for general decision making problems as well. In a previous paper, we proposed an exhaustive method to acquire user trade-off preferences. In this paper, we developed another method to remove the limitation of the high user workload of the exhaustive method. Although we cannot say that it can exactly capture user trade-off preferences, it models the main commonalities of trade-off relations and reflects users' individualities as well.