Optimization of Decision-Making in Artificial Life Model Based on Fuzzy Cognitive Maps

Abstract

The paper describes a new approach to the modelling of the individual-based artificial life model based on fuzzy cognitive maps (FCM). The proposed concept focuses on the optimization of artificial intelligence of individuals in multi-agent models and their adaptation to environment. In this process of optimization, emphasis is put on the decision-making method. FCM offers great complexity and learning through evolutionary algorithms. However, too large FCMs suffer from performance issues. Therefore, this paper presents a possibility to replace a decision-making part of large FCM with the analytic hierarchy process (AHP) method, which is widely used, especially for decision support. In comparison with the large FCM model, a combination with AHP provides a model with lower computational demands while keeping nearly the same complexity.