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Fast and Loose Semantics for Computational Cognition

机译:用于计算认知的快速和宽松语义

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Psychological evidence supporting the profound effortlessness (and often substantial carelessness) with which human cognition copes with typical daily life situations abounds. In line with this evidence, we propose a formal semantics for computational cognition that places emphasis on the existence of naturalistic and unpretentious algorithms for representing, acquiring, and manipulating knowledge. At the heart of the semantics lies the realization that the partial nature of perception is what ultimately necessitates and hinders-cognition. Inexorably, this realization leads to the adoption of a unified treatment for all considered cognitive processes, and to the representation of knowledge via prioritized implication rules. Through discussion and the implementation of an early prototype cognitive system, we argue that such fast and loose semantics may offer a good basis for the development of machines with cognitive abilities.
机译:心理证据支持具有典型日常生活情况的人类认知应对的深刻努力(和通常很大的粗心)。 符合本证据,我们提出了一种正式的语义,用于计算认知,重点是归因于代表,获取和操纵知识的自然主义和未预解算法。 在语义的核心中,认识到感知的部分性质是最终需要和阻碍认知。 不可否认的是,这种实现导致采用所有被认为的认知过程的统一治疗,并通过优先事项暗示规则来表示知识的代表性。 通过讨论和实施早期原型认知系统,我们认为这种快速和松散的语义可以为具有认知能力的机器的开发提供良好的基础。

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