Cognitive Architectures: Conceptual Abstraction and Reasoning

Abstract

This paper explores the development of an algorithm designed to perform conceptual abstraction and reasoning within cognitive architectures, mirroring human cognitive processes. The core of this work lies in a combined approach utilizing knowledge graphs, neural networks, and Bayesian networks. The system aims to capture the complexities of human thought by integrating structured knowledge representation with probabilistic reasoning. The algorithm's architecture is presented, detailing the key components and their interactions. Specifically, the process involves extracting salient features from data using neural networks, formalizing these features within a knowledge graph, and then employing Bayesian networks to infer relationships and make deductions. We demonstrate a framework that addresses a fundamental challenge in artificial intelligence – bridging the gap between symbolic and sub-symbolic reasoning. The proposed approach offers a novel pathway towards creating more robust and adaptable cognitive architectures.