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Semantic Representation of an Algorithm Knowledge Profile in Metacognitive Architecture CARINA

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dc.contributor.authorGómez Salgado, Adán Albertospa
dc.contributor.authorMadera Cogollo, Hilda Maríaspa
dc.contributor.authorArroyo Durango, Estefanyspa
dc.date.accessioned2019-09-30T16:01:17Zspa
dc.date.available2019-09-30T16:01:17Zspa
dc.date.issued2019-08-09spa
dc.description.degreelevelPregradospa
dc.description.degreenameLicenciado(a) en Informáticaspa
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.format.mimetypeapplication/pdfspa
dc.identifier.urihttps://repositorio.unicordoba.edu.co/handle/ucordoba/1735spa
dc.language.isoengspa
dc.publisher.facultyFacultad de Educación y Ciencias Humanasspa
dc.publisher.programLicenciatura en Informática y Medios Audiovisualesspa
dc.relation.referencesBadre, D., & Wagner, A. (2002). Semantic retrieval, mnemonic control, and prefrontal cortex.spa
dc.relation.referencesBehavioral and cognitive neuroscience reviews, vol. 1, no. 3. pp. 206– 218.spa
dc.relation.referencesBaldoni, 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
dc.relation.referencesBarrera, 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
dc.relation.referencesBrachman, R.J., & Levesque H.J. (2004). Expressing Knowledge. Knowledge Representation and Reasoning.spa
dc.relation.referencesCaro, 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
dc.relation.referencesCaro, 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
dc.relation.referencesChitil, O., & Luo, Y. (2007). Structure and properties of traces for functional programs. Electronic Notes in Theoretical Computer Science, 176(1), 39-63.spa
dc.relation.referencesCox, M. T. (2005). Metacognition in Computation: A selected research review. Retrieved from.spa
dc.relation.referencesCox, M., Dannenhauer, D., Brown, D., Schmitz, S., Eyorokon, V., etal. (2019). MIDCA, Version 1.4spa
dc.relation.referencesUser Manual and Tutorial for the Metacognitive Integrated Dual-Cycle Architecturespa
dc.relation.referencesCox, M., & Raja, A. (2007). Metareasoning: A manifesto. BBN Technical.spa
dc.relation.referencesCox, M., & Ram, A. 1999. Introspective multistrategy learning: On the construction of learning strategies. Artif. Intell., vol. 112, no. 1, pp. 1–55, Aug.spa
dc.relation.referencesEichenbaum, H. (2000). A cortical hippo-campal system for declarative memory. Nature reviews. Neuroscience, 1(1), 41–50.spa
dc.relation.referencesFlavell, J., & Wellman, H. (1975). full-text.spa
dc.relation.referencesFlórez, M., Caro, M. F., & Gómez, A. (2018). Formal Representation of Introspective Reasoning Trace of a Cognitive Function in CARINA.spa
dc.relation.referencesFodor, J. A. (1975). The language of thought (Vol. 5). Harvard University Press.spa
dc.relation.referencesGhasemzadeh, M. (2010). Constructing Semantic Knowledge Model based on Children Dictionary.spa
dc.relation.referencesHartvigsen, 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
dc.relation.referencesJonker, C. M., & Treur, J. (2008). Analysis of the dynamics of reasoning using multiple representations. Artificial Intelligence Preprint Series, 36.spa
dc.relation.referencesKotseruba, 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
dc.relation.referencesNeal, 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
dc.relation.referencesOliveira, E., Mouta, F., & Rocha, A. P. (1993). Negotiation and Conflict Resolution within a Community of Cooperative Agents. Retrieved fromspa
dc.relation.referencesPezzulo, G., & Calvi, G. (2004). A pandemonium can have goals. Proc. Sixth Int. Conf. Cogn. Model. Piccinini,spa
dc.relation.referencesPiccinini, G. (2010). Computation in physical systems.spa
dc.relation.referencesRao, A., & Georgeff, M. (1991). Modeling rational agents within a BDI-architecture. vol. 91, pp. 473-484.spa
dc.relation.referencesScheutz, 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
dc.relation.referencesIntell. Eng., vol. 11, no. 2, pp. 167–172, Apr.spa
dc.relation.referencesSun R, (2003). A Tutorial on CLARION 5.0.spa
dc.relation.referencesSun, R., Zhang, X., & Mathews, R. (2005a). Modeling Meta-Cognition in a Cognitive Architecture. Retrieved fromspa
dc.rightsCopyright Universidad de Córdoba, 2019spa
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccessspa
dc.rights.creativecommonsAtribución 4.0 Internacional (CC BY 4.0)spa
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/spa
dc.subjectSemanticaspa
dc.subjectAlgoritmospa
dc.subjectArquitecturaspa
dc.subject.keywordsSemanticspa
dc.subject.keywordsAlgorithmspa
dc.subject.keywordsArchitecturespa
dc.titleSemantic Representation of an Algorithm Knowledge Profile in Metacognitive Architecture CARINAspa
dc.typeTrabajo de grado - Pregradospa
dc.type.coarhttp://purl.org/coar/resource_type/c_7a1fspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/bachelorThesisspa
dc.type.redcolhttps://purl.org/redcol/resource_type/TPspa
dc.type.versioninfo:eu-repo/semantics/submittedVersionspa
dspace.entity.typePublication
oaire.accessrightshttp://purl.org/coar/access_right/c_16ecspa
oaire.versionhttp://purl.org/coar/version/c_71e4c1898caa6e32spa
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