Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Rational Communication in Multi-Agent Environments
Autonomous Agents and Multi-Agent Systems
Perspectives on multiagent learning
Artificial Intelligence
A Study of an Approach to the Collective Iterative Task Allocation Problem
IAT '07 Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology
The lumière project: Bayesian user modeling for inferring the goals and needs of software users
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Mutual state-based capabilities for role assignment in heterogeneous teams
Proceedings of the 3rd International Symposium on Practical Cognitive Agents and Robots
Modeling and learning synergy for team formation with heterogeneous agents
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Multi-agent team formation: diversity beats strength?
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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A major challenge in the field of Multi-Agent Systems (MAS) is to enable autonomous agents to allocate tasks and resources efficiently. This paper studies an extended approach to a problem we refer to as the Collective Iterative Allocation (CIA) problem. This problem involves a group of agents that progressively refine allocations of teams to tasks. This paper considers the case where the performance of a team is variable and non-deterministic. This requires that each agent is able to maintain and update its probabilistic models using observations of each team's performance. A key result is that each agent needs the capacity to store only two or three observations of a team's performance to find near optimal allocations, and a further increase of this capacity will reduce the number of reallocations significantly.