Multiagent negotiation under time constraints
Artificial Intelligence
Bayesian learning in negotiation
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Bargaining theory with applications
Bargaining theory with applications
Optimal Negotiation Strategies for Agents with Incomplete Information
ATAL '01 Revised Papers from the 8th International Workshop on Intelligent Agents VIII
Bilateral Negotiation Decisions with Uncertain Dynamic Outside Options
WEC '04 Proceedings of the First IEEE International Workshop on Electronic Contracting
Learning models of intelligent agents
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Learning other agents' preferences in multiagent negotiation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Incorporating opponent models into adversary search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Multiagent and Grid Systems - Negotiation and Scheduling Mechanisms for Multiagent Systems
LEARNING DRIFTING NEGOTIATIONS
Applied Artificial Intelligence
Incrementally Refined Acquaintance Model for Consortia Composition
CIA '08 Proceedings of the 12th international workshop on Cooperative Information Agents XII
An Information-Theoretic Approach to Model Identification in Interactive Influence Diagrams
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Facing the challenge of human-agent negotiations via effective general opponent modeling
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Bounded practical social reasoning in the ESB framework
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Proceedings of the 11th International Conference on Electronic Commerce
Model identification in interactive influence diagrams using mutual information
Web Intelligence and Agent Systems
Pairwise issue modeling for negotiation counteroffer prediction using neural networks
Decision Support Systems
A novel strategy for efficient negotiation in complex environments
MATES'12 Proceedings of the 10th German conference on Multiagent System Technologies
ABiNeS: An Adaptive Bilateral Negotiating Strategy over Multiple Items
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Optimizing complex automated negotiation using sparse pseudo-input gaussian processes
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
An efficient automated negotiation strategy for complex environments
Engineering Applications of Artificial Intelligence
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In many negotiation and bargaining scenarios, a particular agent may need to interact repeatedly with another agent. Typically, these interactions take place under incomplete information, i.e., an agent does not know exactly which offers may be acceptable to its opponent or what other outside options are available to that other agent. In such situations, an agent can benefit by learning its opponent's decision model based on its past experience. In particular, being able to accurately predict opponent decisions can enable an agent to generate offers to optimize its own utility. In this paper, we present a learning mechanism using Chebychev's polynomials by which an agent can approximately model the decision function used by the other agent based on the decision history of its opponent. We study a repeated one-shot negotiation model which incorporates uncertainty about opponent's valuation and outside options. We evaluate the proposed modeling mechanism for optimizing agent utility when negotiating with different class of opponents.