Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Predicting tradeoffs in contract-based distributed scheduling
Predicting tradeoffs in contract-based distributed scheduling
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver
IEEE Transactions on Computers
Advantages of a leveled commitment contracting protocol
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
eMediator: a next generation electronic commerce server
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
Automated Negotiation and Decision Making in Multiagent Environments
EASSS '01 Selected Tutorial Papers from the 9th ECCAI Advanced Course ACAI 2001 and Agent Link's 3rd European Agent Systems Summer School on Multi-Agent Systems and Applications
Cognition, Sociability, and Constraints
Balancing Reactivity and Social Deliberation in Multi-Agent Systems, From RoboCup to Real-World Applications (selected papers from the ECAI 2000 Workshop and additional contributions)
Autonomous dynamic reconfiguration in multi-agent systems: improving the quality and efficiency of collaborative problem solving
A case study of agent-based virtual enterprise modelling
CEEMAS'05 Proceedings of the 4th international Central and Eastern European conference on Multi-Agent Systems and Applications
The pricing strategies for agents in real e-commerce
WINE'05 Proceedings of the First international conference on Internet and Network Economics
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In automated negotiation systems consisting of self-interested agents, contracts have traditionally been binding. Leveled commitment contracts-i.e. contracts where each party can decommit by paying a predetermined penalty were recently shown to improve Pareto efficiency even if agents rationally decommit in Nash equilibrium using inflated thresholds on how good their outside offers must be before they decommit. This paper operationalizes the four leveled commitment contracting protocols by presenting algorithms for using them. Algorithms are presented for computing the Nash equilibrium decommitting thresholds and decommitting probabilities given the contract price and the penalties. Existence and uniqueness of the equilibrium are analyzed. Algorithms are also presented for optimizing the contract itself (price and penalties). Existence and uniqueness of the optimum are analyzed. Using the algorithms we offer a contract optimization service on the web as part of Mediator, our next generation electronic commerce server. Finally, the algorithms are generalized to contracts involving more than two agents.