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Foraging Search: Prototypical Intelligence

机译:觅食搜索:原型智能

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摘要

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了。或者因为笛卡尔可能已经说过,在更多的反思中,“我需要吃饭,因此我想。”觅食的动物反复面临着寻找巨大空间中稀疏分布资源的问题。此外,在真正的不确定性(非间断,复杂和非线性动力学)的条件下,资源可能偶象限性地发生。我断言这个问题是智能解决的规范问题。它的解决方案是更高级智能演进的基础,其中搜索空间包括在皮质结构中编码的概念(对象和关系)。在人类中,通过我们称之为思考的概念空间进行了意识的经验。觅食搜索模型基于采用预期处理的高阶自动发源体系(觅食),以增强其在寻找食物时的成功,同时避免成为食物或在敌对世界中发生意外。我展示了一般觅食搜索问题的半正式描述和解决方案的方法。后者是一种采用动态自适应神经元的脑状结构。物理机器人,MAVric,体现了一些觅食的原则。它学会了导致在动态和非间平环境中找到目标的改进的提示。这种能力基于独特的学习机制,该机制编码神经样处理元件中的因果关系。一个参数,先进的前进,根据觅食模型搜索物理世界中的资源,是广义搜索概念资源的原型,就像我们认为的那样。问题代表了稳态意义上的概念性障碍。解决方案的发现恢复稳态平衡。建立概念性提示和解决方案(资源)之间的链接以及随后使用这些提示来思考准同构问题的解决方案,基本上是觅食思想。它是基本觅食模型的一个非常自然的延伸。

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