Please use this identifier to cite or link to this item: https://repositorio.fei.edu.br/handle/FEI/3462
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dc.rights.licenseCreative Commons "Este é um artigo publicado em acesso aberto sob uma licença Creative Commons (CC BY-NC-ND 4.0.) Fonte: https://www.sciencedirect.com/science/article/pii/S0004370218303424?via%3Dihub. Acesso em 11 nov. 2021.-
dc.contributor.authorHOMEM, THIAGO PEDRO DONADON-
dc.contributor.authorPaulo Santos-
dc.contributor.authorCOSTA, ANNA HELENA REALI-
dc.contributor.authorReinaldo Bianchi-
dc.contributor.authorMANTARAS, RAMON LOPEZ DE-
dc.date.accessioned2021-11-11T20:36:06Z-
dc.date.available2021-11-11T20:36:06Z-
dc.date.issued2020-03-20-
dc.identifier.citationHOMEM, T. P. D.; SANTOS, P. E.;COSTA, A. H. R.; BIANCHI, R. A.DA C.; MANTARA, R. LOPEZ DE. Qualitative case-based reasoning and learning. Artificial Intelligence, v. 283, p. 103258, 2020.-
dc.identifier.issn0004-3702-
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/3462-
dc.description.abstractThe development of autonomous agents that perform tasks with the same dexterity as performed by humans is one of the challenges of artificial intelligence and robotics. This motivates the research on intelligent agents, since the agent must choose the best action in a dynamic environment in order to maximise the final score. In this context, the present paper introduces a novel algorithm for Qualitative Case-Based Reasoning and Learning (QCBRL), which is a case-based reasoning system that uses qualitative spatial representations to retrieve and reuse cases by means of relations between objects in the environment. Combined with reinforcement learning, QCBRL allows the agent to learn new qualitative cases at runtime, without assuming a pre-processing step. In order to avoid cases that do not lead to the maximum performance, QCBRL executes case-base maintenance, excluding these cases and obtaining new (more suitable) ones. Experimental evaluation of QCBRL was conducted in a simulated robot-soccer environment, in a real humanoid-robot environment and on simple tasks in two distinct gridworld domains. Results show that QCBRL outperforms traditional RL methods. As a result of running QCBRL in autonomous soccer matches, the robots performed a higher average number of goals than those obtained when using pure numerical models. In the gridworlds considered, the agent was able to learn optimal and safety policies.-
dc.relation.ispartofARTIFICIAL INTELLIGENCE-
dc.rightsAcesso Aberto-
dc.subjectCase-based reasoning-
dc.subjectQualitative spatial reasoning-
dc.subjectReinforcement learning-
dc.subjectRobot soccer-
dc.titleQualitative case-based reasoning and learningpt_BR
dc.typeArtigopt_BR
dc.identifier.doi10.1016/j.artint.2020.103258-
dc.contributor.authorOrcidhttps://orcid.org/0000-0001-8484-0354-
dc.contributor.authorOrcidhttps://orcid.org/0000-0001-9097-827X-
dc.description.volume283-
dc.description.firstpage103258-
fei.source.urlhttps://www.sciencedirect.com/science/article/pii/S0004370218303424?via%3Dihub-
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