Propositional Reasoning for an Embodied Cognitive Model

Jerusa MarchiGuilherme Bittencourt

In this paper we describe the learning and reasoning mechanisms of a cognitive model based on the systemic approach and on the autopoiesis theory. These mechanisms assume perception and action capabilities that can be captured through propositional symbols and uses logic for representing environment knowledge. The logical theories are represented by their conjunctive and disjunctive normal forms. These representations are enriched to contain annotations that explicitly store the relationship among the literals and (dual) clauses in both forms. Based on this representation, algorithms are presented that learn a theory from the agent's experiences in the environment and that are able to determine the robustness degree of the theories given an assignment representing the environment state.

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