Technical Note: \cal Q-Learning
Machine Learning
Economic principles of multi-agent systems
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Software agents
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
The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
General principles of learning-based multi-agent systems
Proceedings of the third annual conference on Autonomous Agents
Using collective intelligence to route Internet traffic
Proceedings of the 1998 conference on Advances in neural information processing systems II
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A Roadmap of Agent Research and Development
Autonomous Agents and Multi-Agent Systems
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Collective Intelligence and Braess' Paradox
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
The design of collectives of agents to control non-Markovian systems
Eighteenth national conference on Artificial intelligence
Proceedings of the 35th conference on Winter simulation: driving innovation
Handling Communication Restrictions and Team Formation in Congestion Games
Autonomous Agents and Multi-Agent Systems
Controlled experimentation with agents: models and implementations
ESAW'04 Proceedings of the 5th international conference on Engineering Societies in the Agents World
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The "Collective Intelligence" (COIN) framework concerns the design of collectives of agents so that as those agents strive to maximize their individual utility functions, their interaction causes a provided "world" utility function concerning the entire collective to be also maximized. Here we show how to extend that framework to scenarios having Markovian dynamics when no re-evolution of the system from counter-factual initial conditions (an often expensive calculation) is permitted. Our approach transforms the(time-extended) argument of each agent's utility function before evaluating that function. This transformation has benefits in scenarios not involving Markovian dynamics, in particular scenarios where not all of the arguments of an agent's utility function are observable. We investigate this transformation in simulations involving both linear and quadratic (nonlinear) dynamics. In addition, we find that a certain subset of these transformations, which result in utilities that have low "opacity (analogous to having high signal to noise) but are not "factored" (analogous to not being incentive compatible), reliably improve performance over that arising with factored utilities. We also present a Taylor Series method for the fully general nonlinear case.