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. Qualitative relations between objects, represented in terms of the EOPRA formalism, are stored as qualitative cases that are applied in the definition of new retrieval and reuse algorithms. The retrieval algorithm uses a Conceptual Neighborhood Diagram to compute the similarity between a new problem and the cases in the case base, and to select the most similar case. The reuse algorithm uses a composition algorithm to calculate the adapted position of the agents based on their frame of reference. The proposed approach was evaluated on simulation and on real humanoid robots. Results suggest that this proposal is faster than using a quantitative model with numerical similarity measurement 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.

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Abstract. 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.

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