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Fuzzy learning in Zamin artificial world

机译：Zamin人工世界中的模糊学习

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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.
机译：模糊逻辑是用于语言知识表示的一种合适方法，而人工生命（A-Life）是致力于研究生命和类似于现实世界的生物合成的研究领域。因此，由于我们对生命的理解更多是语言形式而不是精确数字，所以模糊逻辑似乎是人造生物的合适逻辑。在本文中，我们尝试将模糊逻辑应用于A-life环境，并研究了使用模糊头脑时其生物的能力变化。为此，我们选择了一个名为Zamin的A-life模型，该模型的生物具有基于案例的推理机脑，必须学习其环境规则，寻找食物，避免掠食者并保持其物种。然后，我们用模糊自组织控制器替换了他们的大脑，并将替换结果与以前的生物进行了比较。比较是在单独的环境中进行的，在该环境中记录并比较了成功生活的度量；在公共场所中，它们需要竞争资源并逃避掠食者。

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