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首页> 外文期刊>Journal of Artificial General Intelligence >Combining Evolution and Learning in Computational Ecosystems
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Combining Evolution and Learning in Computational Ecosystems

机译:计算生态系统中的进化与学习相结合

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Although animals such as spiders, fish, and birds have very different anatomies, the basic mechanisms that govern their perception, decision-making, learning, reproduction, and death have striking similarities. These mechanisms have apparently allowed the development of general intelligence in nature. This led us to the idea of approaching artificial general intelligence (AGI) by constructing a generic artificial animal (animat) with a configurable body and fixed mechanisms of perception, decision-making, learning, reproduction, and death. One instance of this generic animat could be an artificial spider, another an artificial fish, and a third an artificial bird. The goal of all decision-making in this model is to maintain homeostasis. Thus actions are selected that might promote survival and reproduction to varying degrees. All decision-making is based on knowledge that is stored in network structures. Each animat has two such network structures: a genotype and a phenotype. The genotype models the initial nervous system that is encoded in the genome (“the brain at birth”), while the phenotype represents the nervous system in its present form (“the brain at present”). Initially the phenotype and the genotype coincide, but then the phenotype keeps developing as a result of learning, while the genotype essentially remains unchanged. The model is extended to ecosystems populated by animats that develop continuously according to fixed mechanisms for sexual or asexual reproduction, and death. Several examples of simple ecosystems are given. We show that our generic animat model possesses general intelligence in a primitive form. In fact, it can learn simple forms of locomotion, navigation, foraging, language, and arithmetic.
机译:尽管蜘蛛,鱼和鸟等动物的解剖结构非常不同,但是控制它们的感知,决策,学习,繁殖和死亡的基本机制却有着惊人的相似之处。这些机制显然允许自然界中一般情报的发展。这导致我们想到了通过构造具有可配置的身体和固定的感知,决策,学习,繁殖和死亡机制的通用人造动物(animat)来接近人工智能的想法。这种通用动物的一个实例可能是人造蜘蛛,另一个是人造鱼,第三个是人造鸟。该模型中所有决策的目标都是保持体内平衡。因此,选择了可能在不同程度上促进生存和繁殖的行动。所有决策均基于存储在网络结构中的知识。每个有生命的动物都有两个这样的网络结构:基因型和表型。基因型模拟了基因组中编码的初始神经系统(“出生时的大脑”),而表型则代表了当前形式的神经系统(“当前的大脑”)。最初,表型和基因型是重合的,但是后来由于学习的结果,表型不断发展,而基因型基本上保持不变。该模型扩展到由动物组成的生态系统,这些动物根据有性或无性生殖以及死亡的固定机制不断发展。给出了简单生态系统的几个例子。我们证明了我们的一般生命模型具有原始形式的一般智能。实际上,它可以学习运动,导航,觅食,语言和算术的简单形式。

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