Fuzzy learning in Zamin artificial world

Fuzzy logic is a suitable approach for linguistic knowledge representation and Artificial Life (A-Life) is a field of study dedicated to studying life and synthesis of creatures like those of real world. Thus, as our understanding about life is more in linguistic form than exact numbers, fuzzy logic seems to be a suitable logic for artificial creatures. In this paper, we have tried to apply fuzzy logic to an A-life environment and have studied the changes in capabilities of its creatures when using fuzzy minds. To do this, we have selected an A-life model called Zamin, which has creatures with case-based reasoner brains who must learn their environmental rules, search for food, avoid predators and keep up their species. Then we have replaced their brains with fuzzy self-organizing controllers and have compared the results of this replacement with former creatures. The comparisons are done both in separate environments, where measures of successful life are recorded and compared, and in common arenas, where they need to compete for resources and escape predators.

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