Towards Constructive Abduction - Solving Abductive Problems with Constraint Programming
Antoni Ligęza · 2015
Abduction can be considered as a principal way of reasoning for problem solving. Abductive inference consists in generation of hypotheses which explain — or logically imply — the phenomenon under investigation in view of accessible background knowledge and are consistent with all other observations. Looking for such hypotheses is typically performed with a spectrum of trial-and-error or search methods and tools. In case of purely logical statements the hypotheses take the form of a set of facts, both positive and negative ones. For example, in case of model based diagnostic reasoning, such diagnostic hypotheses can be generated by consistency based reasoning with minimal search effort. In more complex cases, where values of certain variables are to be found, pure backtracking search becomes inefficient. In this paper we attempt to put forward such abductive inference into a formal framework of Constraint Programming in order to enable the use of constraint propagation techniques. The main idea behind this approach is to make abduction more constructive. The discussion is illustrated with a diagnostic example of a multiplier-adder system.