Retrieving and reusing qualitative cases: An application in humanoid-robot soccer

Thiago Pedro Donadon Homem, Danilo Hernani Perico, Paulo Eduardo Santos, Reinaldo Augusto Da Costa Bianchi, Ramon Lopez De Mantaras

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


This paper proposes a new Case-Based Reasoning (CBR) approach, named Q-CBR, that uses 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 a 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.

Original languageEnglish
Pages (from-to)251-265
Number of pages15
JournalAI Communications
Issue number3-4
Publication statusPublished - 12 Jul 2017
Externally publishedYes


  • Case-Based Reasoning
  • humanoid-robot
  • Qualitative Spatial Reasoning


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