Effective problem solving through fuzzy logic knowledge bases aggregation

Kolomvatsos, K. (2016) Effective problem solving through fuzzy logic knowledge bases aggregation. Soft Computing, 20(3), pp. 1071-1092. (doi: 10.1007/s00500-014-1568-2)

Abstract

Cooperative problem solving has attracted great interest in the research community. A number of nodes can cooperate to reach a common goal which is the solution to a specific problem. Through a team effort, nodes try to achieve the best possible result. Each node undertakes the responsibility to fulfill a specific task (part of a complex plan towards the solution of the problem) and return the final outcome to a coordinator. The coordinator assigns tasks to nodes and, accordingly, collects the results. In this paper, we propose the adoption of fuzzy logic (FL) in the decision making process of each node. Nodes adopt the provided (by the coordinator) FL knowledge base that indicates the appropriate actions during the problem solving process. This knowledge base is updated during the execution of the assigned task to be fully aligned with the environment characteristics. Partial knowledge bases experienced by the nodes are sent back to the coordinator and are aggregated to generate a ‘global’ knowledge base that incorporates the experience retrieved by the team. We describe the aggregation process and propose two models: the first adopts the immediate distribution of the aggregated FL rule base to the active nodes and the second indicates the future use of the aggregated FL rule base. Our evaluation involves the realization of the proposed framework in a specific research domain as well as numerical results retrieved by a large number of simulations. Our results show that there is a trade-off between the two proposed models concerning the quality of the final solution and the time required to retrieve the final outcome.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Kolomvatsos, Dr Kostas
• Authors: Kolomvatsos, K.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Soft Computing
• Publisher: Springer
• ISSN: 1432-7643
• ISSN (Online): 1433-7479
• Published Online: 08 January 2015