Bayesian learning in negotiation
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
The Influence of Social Dependencies on Decision-Making: Initial Investigations with a New Game
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Learning on opponent's preferences to make effective multi-issue negotiation trade-offs
ICEC '04 Proceedings of the 6th international conference on Electronic commerce
Monotonic concession protocols for multilateral negotiation
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Meta-level coordination for solving negotiation chains in semi-cooperative multi-agent systems
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Learning social preferences in games
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Providing a recommended trading agent to a population: a novel approach
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Constraint-based reasoning and privacy/efficiency tradeoffs in multi-agent problem solving
Artificial Intelligence - Special issue: Distributed constraint satisfaction
Learning other agents' preferences in multiagent negotiation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
A negotiation framework for linked combinatorial optimization problems
Autonomous Agents and Multi-Agent Systems
HOMAN, a learning based negotiation method for holonic multi-agent systems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In this paper we introduce the Semi-Cooperative Extended Incremental Multiagent Agreement Problem with Preferences (SC-EIMAPP). In SC-EIMAPPs, variables arise over time. For each variable, a set of distributed agents gain utility for agreeing on an option to assign to the variable. We define semi-cooperative utility as an agent's privately owned preferences, discounted as negotiation time increases. SC-EIMAPPs reflect real world agreement problems, including meeting scheduling and task allocation.We analyze negotiation in SC-EIMAPPs theoretically. We note that agents necessarily \emph{reveal} information about their own preferences and constraints as they negotiate agreements. We show how agents can use this limited and noisy information to learn to negotiate more effectively. We demonstrate our results experimentally.