An overview about metacognitive expectations in a cognitive agent

Contenido principal del artículo

Autores

Ana María Cardozo Soto Dalia Patricia Madera Doval Adan Alberto Gómez Manuel Fernando Caro Piñeres

Resumen

The purpose of this article is to give general vision through the perspectives of several authors, where they will be highlighted in how metacognitive expectations play an important role within a cognitive agent.

Cognitive agents are conceived as anticipatory systems; they are able to reason about the future and fulfill their goals thanks to their anticipatory representations. a cognitive agent has to be able to conceive and represent (implicitly or explicitly) its future, including its objectives, and to maintain its predictions and representations accurate. Goals are of primary importance for our analysis since they relate anticipatory mechanisms, representations, and future-oriented conducts. internal models, anticipatory representations and goal-directed behavior have always been related in the analysis of cognitive systems.

Detalles del artículo

Referencias

1] Pezzulo, G. (2008). Coordinating with the future: the anticipatory nature of representation. Minds and Machines, 18(2), 179-225.

[2] Bubic, A., Von Cramon, D. Y., & Schubotz, R. I. (2010). Prediction, cognition and the brain. Frontiers in human neuroscience, 4.

[3] Ranathunga, S., Purvis, M., & Cranefield, S. (2011). Integrating expectation handling into Jason.

[4] Kondrakunta, S. (2017). Implementation and Evaluation of Goal Selection in a Cognitive Architecture (Doctoral dissertation, Wright State University).

[5] Castelfranchi, C. (2001). Towards a cognitive memetics: Socio-cognitive mechanisms for memes selection and spreading.

[6] Cox, M. T. (1996). Introspective multistrategy learning: Constructing a learning strategy under reasoning failure (No. TR-GIT-CC-96-06). GEORGIA INST OF TECH ATLANTA.

[7] Sadeh, I., & Zion, M. (2009). The development of dynamic inquiry performances within an open inquiry setting: A comparison to guided inquiry setting. Journal of Research in Science Teaching, 46(10), 1137-1160

[8] Cox, MT (2005). Metacognition in computing: A selected research review. Artificial Intelligence, 169 (2), 104-141.

[9] Cox, MT (2007). Perpetuo self-aware
agentes cognitivos. AI revista , 28 (1), 32.

[10] Fox, S., and Leake, DB (2001). Introspective reasoning for the refinement of the index in case-based reasoning. Journal of Experimental & Theoretical Artificial Intelligence, 13 (1), 63-88.

[11] Cox, M. T. (2011). Metareasoning, monitoring, and self-explanation. Metareasoning: Thinking about thinking, 131-149.

[12] Manvi, S. S., & Kakkasageri, M. S. (2008). Multicast routing in mobile ad hoc networks by using a multiagent system. Information Sciences, 178(6), 1611-1628.

[13] Castelfranchi, C., & Lorini, E. (2003, August). Cognitive anatomy and functions of expectations. In IJCAI03 Workshop on Cognitive Modeling of Multi-Agent Agents and Interactions (pp. 9-11). Morgan Kaufmann Publishers, Acapulco.

[14] Murdock, J. W. (2001). Self-improvement through self-understanding: Model-based reflection for agent adaptation. Georgia Institute of Technology.

[15] Cox, M. T. (1997, July). An explicit representation of reasoning failures. In International Conference on Case-Based Reasoning (pp. 211-222). Springer, Berlin, Heidelberg.

[16] Cox, M. T. (2005). Metacognition in computation: A selected history. In AAAI Spring Symposium: Metacognition in Computation (pp. 1-17).

[17] Castelfranchi, C., & Lorini, E. (2003, August). Cognitive anatomy and functions of expectations. In IJCAI03 Workshop on Cognitive Modeling of Multi-Agent Agents and Interactions (pp. 9-11). Morgan Kaufmann Publishers, Acapulco.

[18] Cox, M. T. (1996). Introspective multistrategy learning: Constructing a learning strategy under reasoning failure (No. TR-GIT-CC-96-06). GEORGIA INST OF TECH ATLANTA.

[19] Cox, M. T. (2005). Metacognition in computation: A selected research review. Artificial intelligence, 169 (2), 104-141.

[20] Shi, Z., & Zhang, S. (2005, August). Introspective learning based on cases. In Cognitive Informatics, 2005. (ICCI 2005). Fourth IEEE Conference on (pp. 43-48). IEEE.

[21] Cox, M. T., & Veloso, M. M. (1997, July). Supporting combined human and machine planning: An interface for planning by analogical reasoning. In International Conference on Case-Based Reasoning (pp. 531-540). Springer, Berlin, Heidelberg.

[22 ]Gudwin, R., Paraense, A., de Paula, S., Fróes, E., Gibaut, W., Castro, E., ... & Raizer, K. The Multipurpose Enhanced Cognitive Architecture (MECA)

[23] Adam, C. (2007). Emotions: from psychological theories to logical formalization and implementation in a BDI agent (Doctoral dissertation).

[24] Sadeh, I., & Zion, M. (2009). The development of dynamic inquiry performances within an open inquiry setting: A comparison to guided inquiry setting. Journal of Research in Science Teaching, 46(10), 1137-1160.

[25] Salvucci, D., Boer, E., & Liu, A. (2001). Toward an integrated model of driver behavior in cognitive architecture. Transportation Research Record: Journal of the Transportation Research Board, (1779), 9-16

[26] Adam, C. (2007). Emotions: from psychological theories to logical formalization and implementation in a BDI agent (Doctoral dissertation).

[27] Castelfranchi, C., & Lorini, E. (2003, August). Cognitive anatomy and functions of expectations. In IJCAI03 Workshop on Cognitive Modeling of Agents and Multi-Agent Interactions(pp. 9-11). Morgan Kaufmann Publishers, Acapulco.

[28] Adam, C. (2007). Emotions: from psychological theories to logical formalization and implementation in a BDI agent(Doctoral dissertation).

[29] Castelfranchi, C. (2001). Towards a cognitive memetics: Socio-cognitive mechanisms for memes selection and spreading.

[30] Kondrakunta, S. (2017). Implementation and Evaluation of Goal Selection in a Cognitive Architecture (Doctoral dissertation, Wright State University).

Descargas

La descarga de datos todavía no está disponible.