Abstract
Psychologically and neurobiologically plausible models of knowledge often must make a difficult choice between distributed and localist representation. Distributed representation can be flexible and hold up well to noisy data, but localist models allow for structured knowledge to be represented unambiguously and reasoned over in rigorous, transparent fashion. We present a way of representing knowledge within the hybrid cognitive architecture CLARION. Our system allows both structured knowledge and distributed knowledge to synergistically coexist while remaining within the limits defined by CLARION'S dual-process framework. After showing how our system can allow more complex knowledge structures to arise, we describe algorithms that use such structures to model many types of reasoning, including: analogical reasoning, deductive reasoning, moral reasoning, and more. We place the structural knowledge afforded CLARION within a formal hierarchy of expressivity for such knowledge, and discuss implications of this work.
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