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.
An example for spatial CBR is Q-CBR (Qualitative Case-Based Reasoning) that has shown promising results using Qualitative Spatial Reasoning (QSR) theory for retrieval in the technical domain of robotics artificial intelligence REF .
Qualitative Case-based Reasoning for Humanoid Robot Soccer: a new retrieval and reuse algorithm
ICCBR
Computer Science