Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Computationally feasible VCG mechanisms
Proceedings of the 2nd ACM conference on Electronic commerce
Universal voting protocol tweaks to make manipulation hard
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Robust coordination to sustain throughput of an unstable agent network
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Coordination to avoid starvation of bottleneck agents in a large network system
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multiagent coordination for controlling complex and unstable manufacturing processes
Expert Systems with Applications: An International Journal
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We consider the problem of coordinating the behavior of multiple self-interested agents. It involves constraint optimization problems that often can only be solved by local search algorithms. Using local search poses problems of incentivecompatibility and individual rationality. We thus define a weaker notion of bounded-rational incentive-compatibility where manipulation is made impossible with high probability through computational complexity. We observe that in real life, manipulation of complex situations is often impossible because the effect of the manipulation cannot be predicted with sufficient accuracy. We show how randomization schemes in local search can make predicting its outcome hard and thus form a bounded-rational incentive-compatible coordination algorithm.