Name
Title | ||
|---|---|---|
Collective iterative allocation: Enabling fast and optimal group decision making: The role of group knowledge, optimism, and decision policies in distributed coordination |
Abstract | ||
|---|---|---|
A major challenge in the field of Multi-Agent Systems is to ena ble autonomous agents to allocate tasks efficiently. This pa per extends previous work on an approach to the collective iterative allocation problem where a group of agents endeavours to find the best allocations possible through refinements of the se allocations over time. For each iteration, each agent pro poses an allocation based on its model of the problem domain, then one of the proposed allocations is selected and executed which enables us to assess if subsequent allocations should be refined. We o ffer an efficient algorithm capturing this process, and then report on theoretical and empirical results that analyse the role of t hree conditions in the performance of the algorithm: accuracy of agents' estimations of the performance of a task, the degree of optimism, and the type of group decision policy that determines which allocation is selected after each proposal phase. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.3233/WIA-2010-0177 | Web Intelligence and Agent Systems: An International Journal |
Keywords | Field | DocType |
collective iterative allocation problem,group decision policy,proposed allocation,optimal group decision,group knowledge,Multi-Agent Systems,subsequent allocation,autonomous agent,agents endeavour,best allocation,efficient algorithm,problem domain | Autonomous agent,Problem domain,Computer science,Operations research,Optimism,Artificial intelligence,Management science,Machine learning,Group decision-making | Journal |
Volume | Issue | Citations |
8 | 1 | 4 |
PageRank | References | Authors |
0.50 | 24 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian Guttmann | 1 | 16 | 2.25 |
| Michael P. Georgeff | 2 | 3998 | 669.02 |
| Iyad Rahwan | 3 | 1346 | 90.64 |