Fast and Loose Semantics for Computational Cognition

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.