Towards a model-theoretic framework for describing the semantic aspects of cognitive processes

  1. MIGUEL TOMÉ, Sergio 1
  1. 1 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

Revista:
ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal

ISSN: 2255-2863

Año de publicación: 2019

Volumen: 8

Número: 4

Páginas: 83-96

Tipo: Artículo

DOI: 10.14201/ADCAIJ2019848396 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal

Resumen

Semantics is one of the most challenging aspects of cognitive architectures. Mathematical logic, or linguistics, highlights that semantics is essential to human cognition. The Cognitive Theory of True Conditions (CTTC) is a proposal to implement cognitive abilities and to describe the semantics of symbolic cognitive architectures based on model-theoretic semantics. This article focuses on the concepts supporting the mathematical formulation of the CTTC, its relationship to other proposals, and how it can be used as a framework for designing cognitive abilities in agents.

Referencias bibliográficas

  • Barsalou, L., 2008. Grounded Cognition. Annual Review of Psychology, 59(1):617–645.
  • Barsalou, L., 2010. Grounded Cognition: Past, Present, and Future. Topics in Cognitive Science, 2(4):716–724. Brachman, R. and Levesque, H., 2004. Knowledge Representation and Reasoning. Morgan Kaufmann.
  • Davidson, D., 1967. Truth and Meaning. Synthese, 17(3):304–323. Davidson, D., 2005. Truth and Predication. Harvard University Press.
  • Gödel, K., 1931. Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I.
  • Monatshefte für Mathematik und Physik, 38(1):173–198.
  • Jacques, P. et al., 2018. Remembering and imagining alternative versions of the personal past. Neuropsychologia, 110:170–179.
  • Johnson-Laird, P., 1980. Mental Models in Cognitive Science. Cognitive Science, 4(1):71–115.
  • Kotseruba, I. and Tsotsos, J., 2020. 40 years of cognitive architectures: core cognitive abilities and practical applications. Artificial Intelligence Review, (53):17–94.
  • Langley, P. et al., 2009. Cognitive architectures: Research issues and challenges. Cognitive Systems Research, 10(2):141–160.
  • Manzano, M., 1999. Model Theory. Oxford University Press.
  • Merrick, K., 2017. Value systems for develpmental cognitive robotics: A survey. Cognitive Systems Research, 41:38 – 55.
  • Miguel-Tomé, S., 2006. Principios matemáticos del pensamiento natural: Teoría cognitiva de condiciones de verdad. Gráficas Quintanilla.
  • Miguel-Tomé, S., 2017. Principios matemáticos del comportamiento natural. Ph.D. thesis, Universidad de Salamanca.
  • Miguel-Tomé, S., 2018a. Decision-making processes in the Cognitive Theory of True Conditions. ArXiv:1803.02476.
  • Miguel-Tomé, S., 2018b. Multi-optional Many-sorted Past Present Future structures and its description. ArXiv:1801.08212.
  • Miguel-Tomé, S., 2018c. Navigation through unknown and dynamic open spaces using topological notions. Connection Science, 30(2):160–185.
  • Miller, G., 1956. The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information. Psychological Review, 63(2):81–97.
  • Mira-Mira, J. and García-Delgado, A., 2007. On how the computational paradigm can help us to model and interpret the neural function. Nat. Comput., 6(3):211–240.
  • Mueller, E., 2015. Commonsense Reasoning: An Event Calculus Based Approach. Morgan Kaufmann, 2nd edition edition.
  • Newell, A., 1973. You can’t play 20 questions with nature and win. In Visual Information Processing, pages 283–308. Academic Press.
  • Newell, A., 1990. Unified theories of cognition. Harvard University Press.
  • Pezzulo, G. et al., 2013. Computational Grounded Cognition: a new alliance between grounded cognition and computational modeling. Frontiers in Psychology, 3:612.
  • Samsonovich, A., 2010. Toward a unified catalog of implemented cognitive architectures. In Proceeding of the 2010 conference on biologically inspired cognitive architectures, pages 195–244.
  • Sescousse, G. et al., 2013. Processing of primary and secondary rewards: A quantitative meta-analysis and review of human functional neuroimaging studies. Neuroscience & Biobehavioral Reviews, 37(4):681 – 696.
  • Targon, V., 2016. Learning the Semantics of Notational Systems with a Semiotic Cognitive Automaton. Cognitive Computation, 8(4):555–576.
  • Thielscher, M., 2005. Reasoning Robots:The Art and Science of Programming Robotic Agents. Springer Netherlands.
  • Wang, P., 2005. Experience-grounded semantics: a theory for intelligent systems. Cognitive Systems Research, 6(4):282–302.
  • Wang, P., 2009. Analogy in a general-purpose reasoning system. Cognitive Systems Research, 10(3):286–296.
Fuente de los datos: Dialnet