Case-Based Reasoning, Analogy, and Interpolation

Béatrice Fuchs (1, 2) , Jean Lieber (3) , Laurent Miclet (4) , Alain Mille (1, 2) , Amedeo Napoli (5) , Henri Prade (6, 7) , Gilles Richard (6)

1 LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 TWEAK - Traces, Web, Education, Adaptation, Knowledge
3 Data Science, Knowledge, Reasoning and Engineering - K team
4 IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires
5 ORPAILLEUR - Knowledge representation, reasonning
6 IRIT-ADRIA - Argumentation, Décision, Raisonnement, Incertitude et Apprentissage
7 CNRS - Centre National de la Recherche Scientifique

This chapter presents several types of reasoning based on analogy and similarity. Case-based reasoning, presented in Section 2, consists in searching a case (where a case represents a problem-solving episode) similar to the problem to be solved and to adapt it to solve this problem. Section 3 is devoted to analogical reasoning and to recent developments based on analogical proportion. Interpolative reasoning, presented in Section 4 in the formal setting of fuzzy set representations, is another form of similarity-based reasoning.

Domaines: Intelligence artificielle [cs.AI]

Béatrice Fuchs, Jean Lieber, Laurent Miclet, Alain Mille, Amedeo Napoli, et al.. Case-Based Reasoning, Analogy, and Interpolation. Pierre Marquis; Odile Papini; Henri Prade. A Guided Tour of Artificial Intelligence Research, Volume I: Knowledge Representation, Reasoning and Learning, Springer International Publishing, pp.307-339, 2020, A Guided Tour of Artificial Intelligence Research, 978-3-030-06163-0. ⟨10.1007/978-3-030-06164-7_10⟩. ⟨hal-03119305⟩