Author:
Antoni Ligęza
Affiliation:
AGH - University of Science and Technology, Poland
Keyword(s):
Abduction, Constraint Programming, Model-based Diagnosis, Consistency-based Reasoning.
Related
Ontology
Subjects/Areas/Topics:
Applications and Case-studies
;
Artificial Intelligence
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Domain Analysis and Modeling
;
Health Engineering and Technology Applications
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
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.
(More)