Abstract
A major challenge in the field of Multi-Agent Systems (MAS) is to enable autonomous agents to allocate tasks and resources efficiently. This paper studies an extended approach to a problem we refer to as the Collective Iterative Allocation (CIA) problem. This problem involves a group of agents that progressively refine allocations of teams to tasks. This paper considers the case where the performance of a team is variable and non-deterministic. This requires that each agent is able to maintain and update its probabilistic models using observations of each team’s performance. A key result is that each agent needs the capacity to store only two or three observations of a team’s performance to find near optimal allocations, and a further increase of this capacity will reduce the number of reallocations significantly.
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Keywords
- Solution Quality
- Computational Requirement
- Benchmark Setting
- Performance Variability
- Stability Indicator
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Guttmann, C. (2008). Making Allocations Collectively: Iterative Group Decision Making under Uncertainty. In: Bergmann, R., Lindemann, G., Kirn, S., Pěchouček, M. (eds) Multiagent System Technologies. MATES 2008. Lecture Notes in Computer Science(), vol 5244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87805-6_8
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DOI: https://doi.org/10.1007/978-3-540-87805-6_8
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