Engenharia de Robôs
URI permanente desta comunidadehttps://repositorio.fei.edu.br/handle/FEI/339
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- Qualitative case-based reasoning and learning(2020-03-20) HOMEM, THIAGO PEDRO DONADON; Paulo Santos; COSTA, ANNA HELENA REALI; Reinaldo Bianchi; MANTARAS, RAMON LOPEZ DEThe 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.
- Qualitative case-based reasoning for humanoid robot soccer: A new retrieval and reuse algorithm(2016-11-02) HOMEM, T. P. D.; PERICO, D. H.; SANTOS, P. E.; BIANCHI, R.; MANTARA, R. L. Qualitative case-based reasoning for humanoid robot soccer: A new retrieval and reuse algorithm. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), p. 170-185,; PERICO, D. H.; SANTOS, P. E.; Reinaldo Bianchi; MANTARA, R. L.© Springer International Publishing AG 2016.This paper proposes a new Case-Based Reasoning (CBR) approach, named Q-CBR, that uses a Qualitative Spatial Reasoning theory to model, retrieve and reuse cases by means of spatial relations. A qualitative distance and orientation calculus (EOPRA) is used to model cases using qualitative relations between the objects in a case. A new retrieval algorithm is proposed that uses the Conceptual Neighborhood Diagram to compute the similarity measure between a new problem and the cases in the case base. A reuse algorithm is also introduced that selects the most similar case and shares it with other agents, based on their qualitative position. The proposed approach was evaluated on simulation and on real humanoid robots. Preliminary results suggest that the proposed approach is faster than using a quantitative model and other similarity measure such as the Euclidean distance. As a result of running Q-CBR, the robots obtained a higher average number of goals than those obtained when running a metric CBR approach.