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Propositional Reasoning for an Embodied Cognitive Model

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Advances in Artificial Intelligence – SBIA 2004 (SBIA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3171))

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Abstract

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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Author information

Authors and Affiliations

  1. Departamento de Automação e Sistemas, Universidade Federal de Santa Catarina, 88040-900, Florianópolis, SC, Brazil

    Jerusa Marchi & Guilherme Bittencourt

Authors
  1. Jerusa Marchi
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  2. Guilherme Bittencourt
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Editor information

Editors and Affiliations

  1. Instituto de Informática, UFRGS, Porto Alegre, RS, Brasil

    Ana L. C. Bazzan

  2. Intelligent Systems Laboratory LSI, Center of Technology, Federal University of Maranao UFMA, Bacanga Campus, 65080-040, Sao Luis, MA, Brazil

    Sofiane Labidi

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Marchi, J., Bittencourt, G. (2004). Propositional Reasoning for an Embodied Cognitive Model. In: Bazzan, A.L.C., Labidi, S. (eds) Advances in Artificial Intelligence – SBIA 2004. SBIA 2004. Lecture Notes in Computer Science(), vol 3171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28645-5_17

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  • DOI: https://doi.org/10.1007/978-3-540-28645-5_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23237-7

  • Online ISBN: 978-3-540-28645-5

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