Publicado

2018-07-01

Design of metacognitive expectations of cognitive functions through ontological representations

Diseño de expectativas metacognitivas de funciones cognitivas a través de representaciones ontológicas

Palabras clave:

artificial intelligence, expectations, computational metacognition, cognitive architecture CARINA, cognitive agent (en)
inteligencia artificial, expectativas, metacognición computacional, arquitectura cognitiva, agente cognitivo (es)

Autores/as

This paper presents a model of metacognitive expectations about the running time of cognitive functions in the metacognitive architecture CARINA. A formal and ontological representation is created that establishes the relationship between the process of observing a fact in the self-model and a belief stored in the semantic memory of the system. The cognitive ontology evidenced tracing and interchange information process among different kind of memories, such as: sensorial memory, semantic memory, procedural memory, prospective memory and working memory. The experiment carried out demonstrated the functionality of the model where expectations were generated for each observation and could be compared with the observed values in real time. Another type of result was the conceptual advance of an expectation, the formal mathematical representation, the design of the ontology and the model as a mechanism of implementation in CARINA architecture.
Este artículo presenta un modelo de expectativas metacognitivas sobre el tiempo de funcionamiento de las funciones cognitivas en la arquitectura metacognitiva CARINA. Se crea una representación formal y ontológica que establece la relación entre el proceso de observar un hecho en el auto-modelo y una creencia almacenada en la memoria semántica del sistema. La ontología cognitiva evidenció el proceso de búsqueda e intercambio de información entre diferentes tipos de recuerdos, tales como: memoria sensorial, memoria semántica, memoria de procedimientos, memoria prospectiva y memoria de trabajo. El experimento realizado demostró la funcionalidad del modelo en donde se generó expectativas para cada observación y podía compararlas con los valores observados en tiempo real. Otro tipo de resultado fue avance conceptual de una expectativa, la representación matemática formal, el diseño de la ontología y el modelo como un mecanismo de implementación en la arquitectura CARINA.

Descargas

Los datos de descargas todavía no están disponibles.

Citas

Pezzulo, G., Coordinating with the future: the anticipatory nature of representation. Minds and Machines, 18(2), pp. 179-225. 2008. DOI: 10.1007/s11023-008-9095-5

Castelfranchi, C. and Lorini, E., Cognitive anatomy and functions of expectations 1. Artificial Intelligence, (October). 2001.

Castelfranchi, C. and Lorini, E., Cognitive anatomy and functions of expectations. In: IJCAI03 Workshop on Cognitive Modeling of Agents and Multi-Agent Interactions, 2003, pp. 9-11. DOI: 10.1109/IAT.2006.2

Castelfranchi, C., For a systematic theory of expectations. BVAI, 3704, 2007, pp. 258-276. DOI: 10.1007/s11229-006-9156

Haidarian, H., Dinalankara, W., Fults, S., Wilson, S., Perlis, D., Schmill, M.D. and Anderson, M.L., The metacognitive loop : an architecture for building robust intelligent systems. AAAI Fall Symposium, (II), 2010, pp. 33-39.

Sevcik, C., The Effects Andrzej Duda. Information Processing Letters, 16, pp. 221-229,1983.

Sun, J. and Peterson, G.D., An effective execution time approximation method for parallel computing. IEEE Transactions on Parallel and Distributed Systems (TPDS), 23(11), pp. 2024-2032, 2012. DOI: 10.1109/TPDS.2012.21

Fox, S. and Leake, D., Introspective reasoning for index refinement in case-based reasoning. Journal of Experimental & Theoretical Artificial Intelligence, 13(1), pp. 63-88, 2001. DOI: 10.1080/09528130010029794

Wang, Y., On contemporary denotational mathematics for computational intelligence. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5150 LNCS, 2008, pp. 6-29. DOI: 10.1007/978-3-540-87563-5_2

Cox, M.T. and Ashwin, R., Introspective multistrategy learning: on the construction of learning strategies. Artificial Intelligence, 112(1), pp. 1-55. 1999. DOI: 10.1016/S0004-3702(99)00047-8

Caro, M., Josyula, D., Cox, M. and Jiménez, J., Design and validation of a metamodel for metacognition support in artificial intelligent systems. Biologically Inspired Cognitive Architectures, 9, pp. 82-104, 2014. DOI: 10.1016/j.bica.2014.07.002

Piccinini, G., The mind as neural software? Understanding functionalism, computationalism, and computational functionalism. Philosophy and Phenomenological Research, 81(2), pp. 269-311, 2010. DOI: 10.1111/j.1933-1592.2010.00356.x

Fodor, J.A., The language of thought. New York (Vol. 4). 1975. DOI: 10.1016/0093-934X(77)90028-1

Scheutz, M., Computational versus causal complexity. Minds and Machines, 11(4), pp. 543-566, 2001. DOI: 10.1023/A:1011855915651

Machamer, P., Darden, L. and Craver, C.F., Thinking about mechanisms. Philosophy of Science, 67(1), pp. 1-25, 2000. DOI: 10.1086/392759

Cox, M. and Raja, A., Metareasoning: an introduction. In: Cox and Raja (Ed.), In Metareasoning: Thinking about thinking, 2012, pp. 1-23, Cambridge. MA: MIT. DOI: 10.7551/mitpress/9780262014809.003.0001

Cox, M., Oates, T. and Perlis, D., Toward an integrated metacognitive infrastructure. 2011 AAAI Fall Symposium, 2011, pp. 74-81. DOI: 10.1016/j.procs.2014.11.107

Caro, M., Diaz, D. and Josyula, D., MetaThink: a MOF-based metacognitive modeling tool. Ieeexplore.Ieee.Org, pp. 351-358, 2013. DOI: 10.1109/ICCI-CC.2016.7862059

Sun, R., Theoretical status of computational cognitive modeling. Cognitive Systems Research, 10(2), pp. 124-140, 2009. DOI: 10.1016/j.cogsys.2008.07.002

Caro, M., Gomez, A. and Giraldo, J., Algorithmic knowledge profiles for introspective monitoring in artificial cognitive agents. In: 2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), 2017, pp. 475-481. DOI: 10.1109/ICCI-CC.2017.8109792

Cox, M., Introspective multistrategy learning: constructing a learning strategy under reasoning failure. Doctoral dissertation - Cox - 1996.

Gruber, T.R., A translation approach to portable ontology specifications. Knowledge Acquisition, 5(2), pp. 199-220, 1993. DOI: 10.1006/knac.1993.1008

Gómez, A. and Benjamins, R., Overview of knowledge sharing and reuse components: ontologies and problem-solving methods. In: IJCAI-99 Workshop on Ontologies and Problem-Solving Method (KRR5), 1999, pp. 1-15. DOI: 10.1.1.39.249.

Stojanovic, L., Maedche, A., Motik, B. and Stojanovic, N., User-driven ontology evolution management. In: Knowledge Engineering and Knowledge Management: Ontologies and the Semantic Web, 2002, pp. 133-140. DOI:10.1145/637411.637413

Malviya, N., Mishra, N. and Sahu, S., Developing university ontology using protégé OWL tool: process and reasoning. International Journal of Scientific & Engineering Research, 2(9), pp. 1-8, 2011.

Gordon, A., Hobbs, J. and Cox, M., Anthropomorphic self-models for metareasoning agents. Metareasoning: Thinking about Thinking, 129-135. 2011. DOI: 10.7551/mitpress/9780262014809.003.0019

La Serna, N., Un analizador sintático eficiente para gramáticas del español, Rev. investig. sist. inform., 1(1), pp. 19-26, 2004.

Zhang, Z.J., Shi, T.Y. and Yu, Z.H., A method for building geographical conditions ontology attribute library: taking Tianjin as an example. Applied Mechanics and Materials, 156, pp. 411-414, 2013. DOI: 10.4028/www.scientific.net/AMM.411-414.156

Cox, M., Introspective multistrategy learning: constructing a learning strategy under reasoning failure, Thesis Dr. of Philosophy in Computer, Science Georgia Institute of Technology Introspective Mu. 1996b.