Game theory, on-line prediction and boosting
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Regret bounds for prediction problems
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Sharing the cost of multicast transmissions
Journal of Computer and System Sciences - Special issue on Internet algorithms
Proceedings of the fifteenth annual ACM symposium on Parallel algorithms and architectures
Convex Optimization
Experiments with planning and markets in multiagent systems
ACM SIGecom Exchanges
A BGP-based mechanism for lowest-cost routing
Distributed Computing - Special issue: PODC 02
Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing
Reinforcement learning with utility-aware agents for market-based resource allocation
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Distributed planning in hierarchical factored MDPs
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
On Fixed Convex Combinations of No-Regret Learners
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Lazy auctions for multi-robot collision avoidance and motion control under uncertainty
AAMAS'11 Proceedings of the 10th international conference on Advanced Agent Technology
Lagrangian Relaxation for Large-Scale Multi-agent Planning
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
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We develop a novel mechanism for coordinated, distributed multiagent planning. We consider problems stated as a collection of single-agent planning problems coupled by common soft constraints on resource consumption. (Resources may be real or fictitious, the latter introduced as a tool for factoring the problem). A key idea is to recast the distributed planning problem as learning in a repeated game between the original agents and a newly introduced group of adversarial agents who influence prices for the resources. The adversarial agents benefit from arbitrage: that is, their incentive is to uncover violations of the resource usage constraints and, by selfishly adjusting prices, encourage the original agents to avoid plans that cause such violations. If all agents employ no-regret learning algorithms in the course of this repeated interaction, we are able to show that our mechanism can achieve design goals such as social optimality (efficiency), budget balance, and Nash-equilibrium convergence to within an error which approaches zero as the agents gain experience. In particular, the agents' average plans converge to a socially optimal solution for the original planning task. We present experiments in a simulated network routing domain demonstrating our method's ability to reliably generate sound plans.