TY - GEN
T1 - Qualitative case-based reasoning for humanoid robot soccer
T2 - 24th International Conference on Case-Based Reasoning Research and Development, ICCBR 2016
AU - Homem, Thiago P.D.
AU - Perico, Danilo H.
AU - Santos, Paulo E.
AU - Bianchi, Reinaldo A.C.
AU - de Mantaras, Ramon L.
PY - 2016/9/29
Y1 - 2016/9/29
N2 - 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.
AB - 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.
KW - Case-based reasoning
KW - Humanoid robots
KW - Qualitative spatial reasoning
UR - http://www.scopus.com/inward/record.url?scp=84994860566&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-47096-2_12
DO - 10.1007/978-3-319-47096-2_12
M3 - Conference contribution
AN - SCOPUS:84994860566
SN - 9783319470955
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 170
EP - 185
BT - Case-Based Reasoning Research and Development - 24th International Conference, ICCBR 2016, Proceedings
A2 - Belen Diaz-Agudo, M.
A2 - Roth-Berghofer, Thomas
A2 - Goel, Ashok
PB - Springer-Verlag
Y2 - 31 October 2016 through 2 November 2016
ER -