2013/03/27 by Henri Prade, Farrency, Henri, Prade, Henri
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Constraint Satisfaction and Optimization #Data Management and Algorithms #FOS: Computer and information sciences #Logic, Reasoning, and Knowledge #Multi-Criteria Decision Making
paper · pdf · doi:10.48550/arxiv.1304.1502
openalex publication_date 2013/03/27 · openalex created_date 2022/10/02 · openalex updated_date 2026/07/28
This paper presents an approach for developing the explanation capabilities of rule-based expert systems managing imprecise and uncertain knowledge. The treatment of uncertainty takes place in the framework of possibility theory where the available information concerning the value of a logical or numerical variable is represented by a possibility distribution which restricts its more or less possible values. We first discuss different kinds of queries asking for explanations before focusing on the two following types : i) how, a particular possibility distribution is obtained (emphasizing the main reasons only) ; ii) why in a computed possibility distribution, a particular value has received a possibility degree which is so high, so low or so contrary to the expectation. The approach is based on the exploitation of equations in max-min algebra. This formalism includes the limit case of certain and precise information.