A hybrid approach to learn, retrieve and reuse qualitative cases
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2017-11-10
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HOMEM, T. P. D.
PERICO, D. H.
SANTOS, P. E.
COSTA, A. H. R.
Reinaldo Bianchi
DE MANTARAS, R. L.
PERICO, D. H.
SANTOS, P. E.
COSTA, A. H. R.
Reinaldo Bianchi
DE MANTARAS, R. L.
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Proceedings - 2017 LARS 14th Latin American Robotics Symposium and 2017 5th SBR Brazilian Symposium on Robotics, LARS-SBR 2017 - Part of the Robotics Conference 2017
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HOMEM, T. P. D.; PERICO, D. H.; SANTOS, P. E.; COSTA, A. H. R.; BIANCHI, R.; DE MANTANRAS, R. L. A hybrid approach to learn, retrieve and reuse qualitative cases. Proceedings - 2017 LARS 14th Latin American Robotics Symposium and 2017 5th SBR Brazilian Symposium on Robotics, LARS-SBR 2017 - Part of the Robotics Conference 2017, p. 1-6, Nov. 2017.
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© 2017 IEEE.The application of Artificial Intelligence methods is becoming indispensable in several domains, for instance in credit card fraud detection, voice recognition, autonomous cars and robotics. However, some methods fail in performances or solving some problems, and hybrid approaches can outperform the results when compared to traditional ones. In this paper we present a hybrid approach, named qualitative case-based reasoning and learning (QCBRL), that integrates three well-known AI methods: Qualitative Spatial Reasoning, Case-Based Reasoning and Reinforcement Learning. QCBRL system was designed to allow an agent to learn, retrieve and reuse qualitative cases in the robot soccer domain. We applied our method in the Half-Field Offense and we have obtained promising results.