KQML as an agent communication language
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
International Journal of Human-Computer Studies
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
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning
Decision Support Systems and Intelligent Systems
Decision Support Systems and Intelligent Systems
Generating Tradeoffs for Interactive Constraint-Based Configuration
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
CIA '00 Proceedings of the 4th International Workshop on Cooperative Information Agents IV, The Future of Information Agents in Cyberspace
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 the e-Negotiation of Unmatched Logrolling Views
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track1 - Volume 1
On Agent-Mediated Electronic Commerce
IEEE Transactions on Knowledge and Data Engineering
CCL: Expressions of Choice in Agent Communication
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
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
Knowledge-based acquisition of tradeoff preferences for negotiating agents
ICEC '03 Proceedings of the 5th international conference on Electronic commerce
Acquiring domain knowledge for negotiating agents: a case of study
International Journal of Human-Computer Studies
Constructing utility models from observed negotiation actions
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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
Appropriate choice of aggregation operators in fuzzy decision support systems
IEEE Transactions on Fuzzy Systems
Agent-oriented probabilistic logic programming
Journal of Computer Science and Technology - Special section on China AVS standard
A spectrum of compromise aggregation operators for multi-attribute decision making
Artificial Intelligence
EPCE'07 Proceedings of the 7th international conference on Engineering psychology and cognitive ergonomics
Agent-based assistant for e-negotiations
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Applying hybrid case-based reasoning in agent-based negotiations for supply chain management
Expert Systems with Applications: An International Journal
Agent based e-commerce systems that react to buyers' feedbacks -- A fuzzy approach
International Journal of Approximate Reasoning
Agent decision-making in open mixed networks
Artificial Intelligence
Preferences in AI: An overview
Artificial Intelligence
Engineering coordination: selection of coordination mechanisms
AAMAS'11 Proceedings of the 10th international conference on Advanced Agent Technology
KEMNAD: A Knowledge Engineering Methodology For Negotiating Agent Development
Computational Intelligence
Proceedings of the 14th Annual International Conference on Electronic Commerce
Hierarchical Negotiation Model for Complex Problems with Large-Number of Interdependent Issues
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
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A wide range of algorithms have been developed for various types of negotiating agents. In developing such algorithms the main focus has been on their efficiency and their effectiveness. However, this is only a part of the picture. Typically, agents negotiate on behalf of their owners and for this to be effective the agents must be able to adequately represent their owners' strategies and preferences for negotiation. However, the process by which such knowledge is acquired is typically left unspecified. To address this problem, we undertook a study of how user information about negotiation tradeoff strategies and preferences can be captured. Specifically, we devised a novel default-then-adjust acquisition technique. In this, the system firstly does a structured interview with the user to suggest the attributes that the tradeoff could be made between, then it asks the user to adjust the suggested default tradeoff strategy by improving some attribute to see how much worse the attribute being traded off can be made while still being acceptable, and, finally, it asks the user to adjust the default preference on the tradeoff alternatives. This method is consistent with the principles of standard negotiation theory and to demonstrate its effectiveness we implemented a prototype system and performed an empirical evaluation in an accommodation renting scenario. The result of this evaluation indicates the proposed technique is helpful and efficient in accurately acquiring the users' tradeoff strategies and preferences.