ABSTRACT
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.
- Caillou, P., Aknine, S., & Pinson, S. (2002). A multi-agent method for forming and dynamic restructuring of pareto optimal coalitions. Proceedings of the First International Joint Conference on Autonomous Agents and Multi-Agent Systems (pp. 1074--1081). Bologna, Italy: ACM Press. Google ScholarDigital Library
- Dutta, P., & Sen, S. (2002). Emergence of stable coalitions via task exchanges. Proceedings of the First International Joint Conference of Autonomous Agents and Multi-Agent Systems (pp. 312--313). Bologna, Italy: ACM Press. Google ScholarDigital Library
- He, M., Jennings, N., & Leung, H. (2003). On agent-mediated electronic commerce. Knowledge and Data Engineering, IEEE Transactions on, 15, 985--1003. Google ScholarDigital Library
- Saha, S., Sen, S., & Dutta, P. (2003). Helping based on future expectations. Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems (pp. 289--296). Melbourne, Australia: ACM Press. Google ScholarDigital Library
- Sandholm, T. W. (1999). Distributed rational decision making. In G. Weiss (Ed.), Multiagent systems: A modern approach to distributed artificial intelligence, 201--258. Cambridge, MA, USA: The MIT Press. Google ScholarDigital Library
- Sen, S., & Dutta, P. (2000). Searching for the optimal coalition structure. Proceedings of the Fourth International Conference on Multiagent Systems (pp. 286--292). Boston, Massachusetts: IEEE. Google ScholarDigital Library
- Shehory, O. (2003a). Coalition formation: Towards feasible solutions. Proceedings of the 3rd International Central and Eastern European Conference on Multi-Agent Systems (pp. 218--251). Prague, Czech Republic: Springer-Verlag.Google Scholar
- Shehory, O. (2003b). Coalition formation with uncertain heterogeneous information. Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems (pp. 1--8). Melbourne, Australia: ACM Press. Google ScholarDigital Library
- Shehory, O., & Kraus, S. (1999). Feasible formation of coalitions among autonomous agents in nonsuperadditve environments. Computational Intelligence, 15, 218--251.Google ScholarCross Ref
- Srinivas, N., & Deb, K. (1994). Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 2, 221--248.Google ScholarDigital Library
- Tsvetovat, M., Sycara, K., Chen, Y., & Ying, J. (2000). Customer coalitions in the electronic marketplace. Proceedings of the Fourth International Conference on Autonomous Agents (pp. 263--264). Barcelona, Catalonia, Spain: ACM Press. Google ScholarDigital Library
- Zitzler, E., Laumanns, M., & Bleuler, S. (2004). A tutorial on evolutionary multiobjective optimization. Proceedings of the Workshop on Multiple Objective Metaheuristics. Paris, France: Springer-Verlag.Google ScholarCross Ref
- Zitzler, E., Laumanns, M., & Thiele, L. (2001). Spea2: Improving the strength pareto evolutionary algorithm (Technical Report 103). Swiss Federal Institute of Technology, Gloriastrasse 35, CH-8092 Zurich, Switzerland.Google Scholar
- Zitzler, E., & Thiele, L. (1998). An evolutionary algorithm for multiobjective optimization: The strength pareto approach (Technical Report 43). Swiss Federal Institute of Technology, Gloriastrasse 35, CH-8092 Zurich, Switzerland.Google Scholar
- Coalition calculation in a dynamic agent environment
Recommendations
A Plan Based Coalition Formation Model for Multi-agent Systems
WI-IAT '11: Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02This article addresses the coalition formation problem in a multi-agent context where agents plan their activities dynamically and use these plans to coordinate their actions and form suitable coalitions. In most coalition formation methods, when ...
Self-Adaptation-Based Dynamic Coalition Formation in a Distributed Agent Network: A Mechanism and a Brief Survey
In some real systems, e.g., distributed sensor networks, individual agents often need to form coalitions to accomplish complex tasks. Due to communication and computation constraints, it is infeasible for agents to directly interact with all other ...
Integrating self-organisation into dynamic coalition formation
AAMAS '12: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3In some real systems, e.g., sensor networks, individual agents will often need to form coalitions to accomplish complex tasks. Due to communication or computation constrains, it is infeasible for agents to directly interact with all other peers to form ...
Comments