Multi-Agent Collaboration in Competitive Scenarios
For many multi-agent scenarios one can assume that the agents behave cooperatively and contribute to a common goal according to their design. However, our work focuses on competitive scenarios which are characterized by the agents’ strong local interests, their high degree of autonomy, and the lack of global goals. Therefore, two agents will cooperate if, and only if, both will gain — or at least expect to gain — from that cooperation.
This paper presents a conflict resolution mechanism which is appropriate for competitive resource allocation in dynamic environments which is based on compromising. It integrates a goal relaxation mechanism, negotiation histories, and multilateral negotiations.
KeywordsAgents non-cooperative collaboration compromise
- [Fuc96]F. Fuchs. Multi-Agent Collaboration in Competitive Scenarios. In Proc. of the 15’ Workshop of the UK Planning and Scheduling Special Interest Group, Liverpool, 1996.Google Scholar
- [HFL96]S. Hahndel, F. Fuchs, and P. Levi. Distributed Negotiation-based Task Planning for a Flexible Manufacturing Environment. In J. W. Perram and J.-P. Müller, editors, Distributed Software Agents and Applications, number 1069 in Lecture Notes in Artificial Intelligence. Springer, 1996.Google Scholar
- [HH88]B. A. Huberman and T. Hogg. The Behaviour of Computational Ecologies. In B. A. Huberman, editor, The Ecology of Computation, number 2 in Studies in Computer Science and Artificial Intelligence, pages 77–115. North-Holland, 1988.Google Scholar
- [KB91]M. Klein and A. B. Baskin. A Computational Model for Conflict Resolution in Cooperative Design Systems. In S. M. Deen, editor, CKBS’90: Proc. of the International Working Conference on Cooperating Knowledge Based Systems, October 1990, Univ. of Keele, UK, pages 201–219. Springer, Berlin, 1991.Google Scholar
- [LLC91]S. Lander, V. R. Lesser, and M. E. Connell. Conflict Resolution Strategies for Cooperating Expert Agents. In S. M. Deen, editor, CKBS’90: Proc. of the International Working Conference on Cooperating Knowledge Based Systems, October 1990, Univ. of Keele, UK, pages 183–200. Springer, Berlin, 1991.Google Scholar
- [MD88]M. S. Miller and K. E. Drexler. Markets and Computation: Agoric Open Systems. In B. A. Huberman, editor, The Ecology of Computation, number 2 in Studies in Computer Science and Artificial Intelligence, pages 133–176. North-Holland, 1988.Google Scholar
- [PG92]F. Polat and H. A. Guvenir. A Conflict Resolution Based Cooperative Distributed Problem Solving Model. In Proc. of AAAI-92, pages 106–115, 1992.Google Scholar
- [PR94]M. Palatnik and J. S. Rosenschein. Long Term Constraints in Multiagent Negotiation. In 13’ International DAI Workshop, pages 265–279, Seattle, Washington, 1994.Google Scholar
- [Smi88]R. G. Smith. The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver. In A. H. Bond and L. Gasser, editors, Readings in Artificial Intelligence. Morgan Kaufmann Publishers Inc., San Mateo, California, 1988.Google Scholar
- [SRSF90]K. Sycara, S. Roth, N. Sadeh, and M. Fox. Managing Resource Allocation in Multi-Agent Time-Constrained Domains. In Proc. of a Workshop on Innovative Approaches to Planning, Scheduling, Control. Kaufmann, 1990.Google Scholar
- [Wer90]K. Werkman. Design and fabrication problem solving through cooperative agents. Technical report, Lehigh University Bethlehem, 1990.Google Scholar
- [Win93]A. Winkelhofer. Zeitrepräsentation und merkmalsgesteuerte Suche zur Terminplanung. PhD thesis, Technische Universität München, 1993.Google Scholar