Computationally Manageable Combinational Auctions
Management Science
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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
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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
eMediator: a next generation electronic commerce server
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Deliberation Levels in Theoretic-Decision Approaches for Task Allocation in Resource-Bounded Agents
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Sequential Auctions for the Allocation of Resources with Complementarities
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Dynamic Programming
Modeling task allocation using a decision theoretic model
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Team Formation Strategies in a Dynamic Large-Scale Environment
Massively Multi-Agent Technology
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Coordination, Organizations, Institutions and Norms in Agent Systems IV
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GameNets'09 Proceedings of the First ICST international conference on Game Theory for Networks
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In this paper, we address the problem of decision-making under uncertainty for the task selection problem. We consider an environment where an agent has to select tasks to execute in a way which maximizes his gain. The main motivation is the new challenging applications such as planetary rovers, e-commerce, combinatorial auction and vehicle routing where agents are with limited resources and have to distribute and execute a set of tasks under uncertainty. In the model proposed in this paper, we formulate the local task selection as a \textitMarkov Decision Process (MDP). In fact, the MDP allows agents to deal with two sources of uncertainty : (1) the uncertainty on the task allocation, and (2) the uncertainty on the consumption of resources required for executing each task. We will also show how an agent can improve his knowledge about the environment.