Computational geometry: an introduction
Computational geometry: an introduction
Modeling coordination in organizations and markets
Management Science
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Coordination techniques for distributed artificial intelligence
Foundations of distributed artificial intelligence
Methods for task allocation via agent coalition formation
Artificial Intelligence
Geometric spanner for routing in mobile networks
MobiHoc '01 Proceedings of the 2nd ACM international symposium on Mobile ad hoc networking & computing
A Scalable Agent Location Mechanism
ATAL '99 6th International Workshop on Intelligent Agents VI, Agent Theories, Architectures, and Languages (ATAL),
Coalition Formation for Large-Scale Electronic Markets
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Deriving multi-agent coordination through filtering strategies
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Allocating tasks in extreme teams
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Distributed task allocation in social networks
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Team member reallocation via tree pruning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Relevance feedback using weight propagation compared with information-theoretic query expansion
ECIR'07 Proceedings of the 29th European conference on IR research
Distributed model shaping for scaling to decentralized POMDPs with hundreds of agents
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Multiagent task allocation in social networks
Autonomous Agents and Multi-Agent Systems
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We present a distributed algorithm for task allocation in multi-agent systems for settings in which agents and tasks are geographically dispersed in two-dimensional space. We describe a method that enables agents to determine individually how to move so that they are, as a group, efficiently assigned to tasks. The method comprises two algorithms and is especially useful in environments with very large numbers of agent and task nodes. One algorithm adapts computational geometry techniques to determine adjacency information for the agent nodes given the geographical positions of agents and tasks. This adjacency information is used to determine the visible nodes that are most relevant to an agent's decision making process and to eliminate those that it should not consider. The second algorithm uses local heuristics based solely on an agent's adjacent nodes to determine its course of action. This method yields improved task allocations compared to previous algorithms proposed for similar environments. We also present a modification to the second algorithm that improves performance in environments in which multiple agents are required to complete a single task.