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SIGMETRICS '89 Proceedings of the 1989 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
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Coalitions among computationally bounded agents
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Methods for task allocation via agent coalition formation
Artificial Intelligence
Distributed Anonymous Mobile Robots: Formation of Geometric Patterns
SIAM Journal on Computing
Murdoch: publish/subscribe task allocation for heterogeneous agents
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Towards a fault-tolerant multi-agent system architecture
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Algorithm for optimal winner determination in combinatorial auctions
Artificial Intelligence
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Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Computers and Intractability: A Guide to the Theory of NP-Completeness
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Models and algorithmic approaches for cooperative multi-robot systems: thesis abstract
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: doctoral mentoring program
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This paper considers the task reallocation problem, where k agents are to be extracted from a coordinated group of N agents in order to perform a new task. The interaction between the team members and the cost associated with this interaction are represented by a weighted graph. Consider a group of N robots organized in a formation, the graph is the monitoring graph which represents the sensorial capabilities of the robots, i.e., which robot can sense the other and at what cost. Following this example, the team member reallocation problem this paper deals with is the extraction of k robots from the group in order to acquire a new target, while minimizing the cost of the interaction of the remaining group. In general, the method proposed here shifts the utility from the team member itself to the interaction between the members, and calculates the reallocation according to this interaction utility. We found that this can be done optimally by a deterministic polynomial time algorithm under several constraints, the first constraint is that k = O(log N). We describe several other domains in which this method is applicable.