Anytime coalition structure generation with worst case guarantees
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Time-quality tradeoffs in reallocative negotiation with combinatorial contract types
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
Approaches to winner determination in combinatorial auctions
Decision Support Systems - Special issue on information and computational economics
Argumentation as distributed constraint satisfaction: applications and results
Proceedings of the fifth international conference on Autonomous agents
An Algorithm for Optimal Winner Determination in Combinatorial Auctions
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Solving Combinatorial Auctions Using Stochastic Local Search
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
An Algorithm for Multi-Unit Combinatorial Auctions
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A Framework for Argumentation-Based Negotiation
ATAL '97 Proceedings of the 4th International Workshop on Intelligent Agents IV, Agent Theories, Architectures, and Languages
Searching for Optimal Coalition Structures
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
A kernel-oriented model for coalition-formation in general environments: implementation and results
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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Multiagent researchers have worked on the problem of determining optimal contracts between self-interested agents. In particular, Sandholm et al. [1,8] have both theoretically and experimentally studied the necessity and usefulness of different contract types under the assumption of individually myopically rational contracting. We study a variant of sequential contracting where the goal is to maximize social welfare through a fixed-length sequence of individually rational contracts. The space of possible contract sequences is exponential. We compare a greedy deterministic heuristic with a stochastic genetic algorithm based approach for this optimal sequential contract selection problem. We focus on sub-additive domains where individually rational contracts are feasible with side payments. We show that the GA-based approach consistently outperforms the deterministic heuristic by generating larger social welfare.