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dc.contributor.authorGómez Salgado, Adán Albertospa
dc.contributor.authorMadera Cogollo, Hilda Maríaspa
dc.contributor.authorArroyo Durango, Estefanyspa
dc.date.accessioned2019-09-30T16:01:17Z
dc.date.available2019-09-30T16:01:17Z
dc.date.issued2019-08-09
dc.identifier.urihttps://repositorio.unicordoba.edu.co/handle/ucordoba/1735
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.rightsCopyright Universidad de Córdoba, 2019spa
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/spa
dc.subjectSemanticaspa
dc.subjectAlgoritmospa
dc.subjectArquitecturaspa
dc.titleSemantic Representation of an Algorithm Knowledge Profile in Metacognitive Architecture CARINAspa
dc.typeTrabajo de grado - Pregradospa
dcterms.bibliographicCitationBadre, D., & Wagner, A. (2002). Semantic retrieval, mnemonic control, and prefrontal cortex.spa
dcterms.bibliographicCitationBehavioral and cognitive neuroscience reviews, vol. 1, no. 3. pp. 206– 218.spa
dcterms.bibliographicCitationBaldoni, M., Baresi, L., & Dastani, M. (2015). Engineering Multi-Agent Systems: Third International Workshop, EMAS 2015, Istanbul, Turkey, May 5, 2015, Revised, Selected, and Invited Papers (Vol. 9318). Springer.spa
dcterms.bibliographicCitationBarrera, M., Caro, M., Gomez, A. & Giraldo, J. C. (2019). Semantic and formal representation of a cognitive model of metacognitive architecture CARINA. IGI global. In press Binder, J. R., & Desai, R. H. (2011).The neurobiology of semantic memory. Trends in Cognitive Sciences.spa
dcterms.bibliographicCitationBrachman, R.J., & Levesque H.J. (2004). Expressing Knowledge. Knowledge Representation and Reasoning.spa
dcterms.bibliographicCitationCaro, M. F., Gómez, A. A., & Giraldo, J. C. (2017). Algorithmic Knowledge Profiles for Introspective Monitoring in Artificial Cognitive Agents. Universidad de Córdoba – Montería, COL Caro, M., Josyula, D., Gomez, A & Kennedy, C. (2018). Introduction to the CARINA metacognitive architecture.spa
dcterms.bibliographicCitationCaro, M. F., Josyula, D. P., Cox, M. T., & Jiménez, J. A. (2014). Design and validation of a metamodel for metacognition support in artificial intelligent systems. Biologically Inspired Cognitive Architectures, 9, 82–104.spa
dcterms.bibliographicCitationChitil, O., & Luo, Y. (2007). Structure and properties of traces for functional programs. Electronic Notes in Theoretical Computer Science, 176(1), 39-63.spa
dcterms.bibliographicCitationCox, M. T. (2005). Metacognition in Computation: A selected research review. Retrieved from.spa
dcterms.bibliographicCitationCox, M., Dannenhauer, D., Brown, D., Schmitz, S., Eyorokon, V., etal. (2019). MIDCA, Version 1.4spa
dcterms.bibliographicCitationUser Manual and Tutorial for the Metacognitive Integrated Dual-Cycle Architecturespa
dcterms.bibliographicCitationCox, M., & Raja, A. (2007). Metareasoning: A manifesto. BBN Technical.spa
dcterms.bibliographicCitationCox, M., & Ram, A. 1999. Introspective multistrategy learning: On the construction of learning strategies. Artif. Intell., vol. 112, no. 1, pp. 1–55, Aug.spa
dcterms.bibliographicCitationEichenbaum, H. (2000). A cortical hippo-campal system for declarative memory. Nature reviews. Neuroscience, 1(1), 41–50.spa
dcterms.bibliographicCitationFlavell, J., & Wellman, H. (1975). full-text.spa
dcterms.bibliographicCitationFlórez, M., Caro, M. F., & Gómez, A. (2018). Formal Representation of Introspective Reasoning Trace of a Cognitive Function in CARINA.spa
dcterms.bibliographicCitationFodor, J. A. (1975). The language of thought (Vol. 5). Harvard University Press.spa
dcterms.bibliographicCitationGhasemzadeh, M. (2010). Constructing Semantic Knowledge Model based on Children Dictionary.spa
dcterms.bibliographicCitationHartvigsen, G., Cao, W., & Bian, C.-G. (1997). Achieving Efficient Cooperation in a Multi-Agent System: the Twin-Base Modeling mSpider View project e-Team Surgery View project Achieving EEcient Cooperation in a Multi-Agent System: the Twin-Base Modeling Achieving EEcient Cooperation in a Multi-Agent S.spa
dcterms.bibliographicCitationJonker, C. M., & Treur, J. (2008). Analysis of the dynamics of reasoning using multiple representations. Artificial Intelligence Preprint Series, 36.spa
dcterms.bibliographicCitationKotseruba, J., Avella, O. J., & Tsotsos, J. K. (2016). A review of 40 years of cognitive architecture research: Focus on perception, attention, learning and applications.spa
dcterms.bibliographicCitationNeal, R. (1992). Connectionist learning of belief networks. Department of Computer Science, University of Toronto, 10 King's College Road, Toronto, Ontario, Canada M5S 1A4.spa
dcterms.bibliographicCitationOliveira, E., Mouta, F., & Rocha, A. P. (1993). Negotiation and Conflict Resolution within a Community of Cooperative Agents. Retrieved fromspa
dcterms.bibliographicCitationPezzulo, G., & Calvi, G. (2004). A pandemonium can have goals. Proc. Sixth Int. Conf. Cogn. Model. Piccinini,spa
dcterms.bibliographicCitationPiccinini, G. (2010). Computation in physical systems.spa
dcterms.bibliographicCitationRao, A., & Georgeff, M. (1991). Modeling rational agents within a BDI-architecture. vol. 91, pp. 473-484.spa
dcterms.bibliographicCitationScheutz, M. (2001). Computational versus causal complexity. Minds and Machines, 11(4), 543–566. Shi, Z.., Zhou, H., & Wang, J. (1997). Applying case-based reasoning to engine oil design. Artif.spa
dcterms.bibliographicCitationIntell. Eng., vol. 11, no. 2, pp. 167–172, Apr.spa
dcterms.bibliographicCitationSun R, (2003). A Tutorial on CLARION 5.0.spa
dcterms.bibliographicCitationSun, R., Zhang, X., & Mathews, R. (2005a). Modeling Meta-Cognition in a Cognitive Architecture. Retrieved fromspa
dc.type.driverinfo:eu-repo/semantics/bachelorThesisspa
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccessspa
dc.rights.creativecommonsAtribuciónspa
dc.thesis.disciplineLicenciatura en Informárticaspa
dc.thesis.levelPregradospa
dc.thesis.nameLicenciado(a) en Informáticaspa
dc.type.dcmi-type-vocabularyTextspa
dc.type.versioninfo:eu-repo/semantics/submittedVersionspa
dc.description.resumenAn algorithmic knowledge profile is a profile that has a local state of a cognitive function as a local algorithmic state. The semantic representation of knowledge is necessary to acquire a mechanism that stores all the information that a cognitive agent receives. CARINA is a metacognitive architecture for cognitive agents, in CARINA a semantic representation is necessary to store the information found in an algorithmic knowledge profile and thus have access to it for the location of reasoning failures. This article presents a semantic representation of an algorithmic knowledge profile. For this representation of semantic knowledge, a belief map was designed that represents the AKP in the CARINA metacognitive architecture, then a prototype was developed that simulates the precept function generating the AKP at the same time as its belief system. The results showed that when entering a high number of words, time and weight increased when saving the information and throwing the json.spa
dc.subject.keywordsSemanticspa
dc.subject.keywordsAlgorithmspa
dc.subject.keywordsArchitecturespa


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