Distributed rational decision making
Multiagent systems
Customer coalitions in the electronic marketplace
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Emergence of stable coalitions via task exchanges
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
A multi-agent method for forming and dynamic restructuring of pareto optimal coalitions
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
On Agent-Mediated Electronic Commerce
IEEE Transactions on Knowledge and Data Engineering
Coalition formation with uncertain heterogeneous information
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Helping based on future expectations
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Searching for Optimal Coalition Structures
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Heuristics for Dealing with a Shrinking Pie in Agent Coalition Formation
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Coalition formation mechanism in multi-agent systems based on genetic algorithms
Applied Soft Computing
Who Works Together in Agent Coalition Formation?
CIA '07 Proceedings of the 11th international workshop on Cooperative Information Agents XI
Task allocation learning in a multiagent environment: Application to the RoboCupRescue simulation
Multiagent and Grid Systems
A robot-environment cooperation architecture for the safety of elderly people at home
ICOST'12 Proceedings of the 10th international smart homes and health telematics conference on Impact Ananlysis of Solutions for Chronic Disease Prevention and Management
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We consider a dynamic market-place of self-interested agents with differing capabilities. A task to be completed is proposed to the agent population. An agent attempts to form a coalition of agents to perform the task. Before proposing a coalition, the agent must determine the optimal set of agents with whom to enter into a coalition for this task; we refer to this activity as coalition calculation. To determine the optimal coalition, the agent must have a means of calculating the value of any given coalition. Multiple metrics (cost, time, quality etc.) determine the true value of a coalition. However, because of conflicting metrics, differing metric importance and the tendency of metric importance to vary over time, it is difficult to obtain a true valuation of a given coalition. Previous work has not addressed these issues. We present a solution based on the adaptation of a multi-objective optimization evolutionary algorithm. In order to obtain a true valuation of any coalition, we use the concept of Pareto dominance coupled with a distance weighting algorithm. We determine the Pareto optimal set of coalitions and then use an instance-based learning algorithm to select the optimal coalition. We show through empirical evaluation that the proposed technique is capable of eliciting metric importance and adapting to metric variation over time.