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Foraging Search: Prototypical Intelligence

机译:搜寻搜寻:原型情报

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We think because we eat. Or as Descartes might have said, on a little more reflection, "I need to eat, therefore I think." Animals that forage for a living repeatedly face the problem of searching for a sparsely distributed resource in a vast space. Furthermore, the resource may occur sporadically and episodically under conditions of true uncertainty (nonstationary, complex and non-linear dynamics). I assert that this problem is the canonical problem solved by intelligence. It's solution is the basis for the evolution of more advanced intelligence in which the space of search includes that of concepts (objects and relations) encoded in cortical structures. In humans the conscious experience of searching through concept space we call thinking. The foraging search model is based upon a higher-order autopoeitic system (the forager) employing anticipatory processing to enhance its success at finding food while avoiding becoming food or having accidents in a hostile world. I present a semi-formal description of the general foraging search problem and an approach to its solution. The latter is a brain-like structure employing dynamically adaptive neurons. A physical robot, MAVRIC, embodies some principles of foraging. It learns cues that lead to improvements in finding targets in a dynamic and nonstationary environment. This capability is based on a unique learning mechanism that encodes causal relations in the neural-like processing element. An argument is advanced that searching for resources in the physical world, as per the foraging model, is a prototype for generalized search for conceptual resources as when we think. A problem represents a conceptual disturbance in a homeostatic sense. The finding of a solution restores the homeostatic balance. The establishment of links between conceptual cues and solutions (resources) and the later use of those cues to think through to solutions of quasi-isomorphic problems is, essentially, foraging for ideas. It is a quite natural extension of the fundamental foraging model.
机译:我们认为是因为我们吃饭。或者像笛卡尔所说的那样,再三思考,“因此,我认为我需要吃饭。”反复觅食为生的动物面临在广阔的空间中寻找稀疏分布的资源的问题。此外,资源可能在真正不确定性(非平稳,复杂和非线性动力学)的条件下偶发和偶发发生。我断言这个问题是智力解决的规范问题。它的解决方案是高级智能发展的基础,其中搜索空间包括以皮质结构编码的概念(对象和关系)的空间。在人类中,通过概念空间进行搜索的自觉体验称为思维。觅食搜索模型基于高级自体系统(觅食者),该系统采用预期处理来增强其寻找食物的成功,同时避免成为食物或在敌对世界中发生事故。我介绍了一般觅食搜索问题的半形式描述及其解决方法。后者是采用动态适应性神经元的类脑结构。物理机器人MAVRIC体现了一些觅食原理。它可以学习线索,从而可以改善在动态和非平稳环境中寻找目标的能力。此功能基于一种独特的学习机制,该机制在类神经处理元素中编码因果关系。提出了一种论点,即按照觅食模型在物理世界中搜索资源,就像我们认为的那样,是对概念性资源进行广义搜索的原型。问题代表了稳态的概念性混乱。找到解决方案可以恢复体内平衡。在概念提示和解决方案(资源)之间建立联系,以及以后使用这些提示来思考准同构问题的解决方案,本质上是在寻求思想。这是基本觅食模型的自然扩展。

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