A hybrid approach to learn, retrieve and reuse qualitative cases
N/D
Tipo de produção
Artigo de evento
Data de publicação
2017-11-10
Texto completo (DOI)
Periódico
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
Editor
Texto completo na Scopus
Citações na Scopus
2
Autores
HOMEM, T. P. D.
PERICO, D. H.
SANTOS, P. E.
COSTA, A. H. R.
Reinaldo Bianchi
DE MANTARAS, R. L.
Orientadores
Resumo
© 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.
Citação
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.
Palavras-chave
Keywords
Assuntos Scopus
Artificial intelligence methods; Autonomous car; Credit card fraud detections; Hybrid approach; Qualitative spatial reasoning; Robot soccer