Qualitative case-based reasoning and learning

Autor(es)

Donadon Homem, Thiago Pedro ^{[1, 2]} ; Santos, Paulo Eduardo ^{[2, 3]} ; Reali Costa, Anna Helena ^[4] ; da Costa Bianchi, Reinaldo Augusto ^[2] ; de Mantaras, Ramon Lopez ^[5]

Afiliação do(s) autor(es)

^[1] IFSP Fed Inst Sao Paulo, Sao Paulo, SP - Brazil

^[2] FEI Univ Ctr FEI, Sao Bernardo Do Campo, SP - Brazil

^[3] Flinders Univ S Australia, Sch Sci & Technol, Adelaide, SA - Australia

^[4] Univ Sao Paulo, Sao Paulo, SP - Brazil

^[5] CSIC Spanish Natl Res Council, Barcelona - Spain

• Tipo de documento: Artigo Científico
• Fonte: ARTIFICIAL INTELLIGENCE; v. 283, JUN 2020.
• Citações Web of Science: 0

Resumo

The 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. (C) 2020 Elsevier B.V. All rights reserved. (AU)

• Processo FAPESP: 16/18792-9 - Descrição, representação e solução de jogos espaciais
• Beneficiário: Paulo Eduardo Santos
• Modalidade de apoio: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE