Incremental Social Learning Applied to a Decentralized Decision-Making Mechanism: Collective Learning Made Faster
2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems (2010)
Sept. 27, 2010 to Oct. 1, 2010
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SASO.2010.28
Positive feedback and a consensus-building procedure are the key elements of a self-organized decision-making mechanism that allows a population of agents to collectively determine which of two actions is the fastest to execute. Such a mechanism can be seen as a collective learning algorithm because even though individual agents do not directly compare the available alternatives, the population is able to select the action that takes less time to perform, thus potentially improving the efficiency of the system. However, when a large population is involved, the time required to reach consensus on one of the available choices may render impractical such a decision-making mechanism. In this paper, we tackle this problem by applying the incremental social learning approach, which consists of a growing population size coupled with a social learning mechanism. The obtained experimental results show that by using the incremental social learning approach, the collective learning process can be accelerated substantially. The conditions under which this is true are described.
Incremental Social Learning, Self-Organization, Opinion Dynamics, Swarm Intelligence, Collective Learning
M. Birattari, T. Stüetzle, M. Dorigo and M. A. Oca, "Incremental Social Learning Applied to a Decentralized Decision-Making Mechanism: Collective Learning Made Faster," 2010 4th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2010)(SASO), Budapest, 2010, pp. 243-252.