Learning and Abstraction in a Fuzzy Artificial World

S.H. Zadeh, S.B. Shouraki, and R. Halavati (Iran)


Artificial Intelligence, Artificial Life, Fuzzy Reasoning, Machine Learning, Case Based Reasoning


Zamin is an artificial world suitable for cognitive studies on the origin of complex behaviors. Zamin's main organisms (called Aryos) live in a simple world and use their Fuzzy CBR-based brains to learn Zamin's living rules and react to their environment, in order to stay alive. Based on previous works on Zamin, we have made some changes on the organisms' behavior and internal structure, in order to increase their abstraction and learning capabilities. We tested the results and found that with respect to old type Aryos, new ones, can overcome to environmental difficulties more easily, and considerably improve their species' population.

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