Truth maintenance systems and their application for verifying Expert System Knowledge Bases

Abstract

Truth maintenance systems (TMSs) were introduced more than ten years ago, but recently there is an explosion of interest in them and their possible applications in different areas. In this paper we discuss truth maintenance from three perspectives: • Truth maintenance as a data base management facility, which was in fact the original intention of the TMS. • Truth maintenance as an infei'ence facility, which provides a way to extend the role of the TMS in solving problems. • Truth maintenance as a verification facility, which illustrates a new and promising application of TMSs in the area of expert systems design. This paper is not intended to provide a complete survey on TMSs, rather it aims to present the basic ideas and functionality of TMS, and to show how different kinds of TMS can be used as a meta-environment for testing Expert System Knowledge Bases, represented as sets of production rules, for anomalies.The paper is addressed to two groups of readers: those who are looking for an introductory survey on TMSs, and those who are interested in non-conventional techniques for Expert System Knowledge Base verification.

Applications of abduction: testing very long qualitative simulations

“…Detailed complexity results for the verification of rule-based systems have been reported by Levy & Rousset [29]. Other ATMS-style validation approaches are all for non-temporal theories [30,31]. None of these studies explore loops within the dependency graph of a rule based.…”

Feature construction for reduction of tabular knowledge-based systems

“…To this end, researchers in the area have attempted to verify and validate knowledge bases through several different methods such as anomaly detection taking a logical point of view (e.g., [30]), truth maintenance systems (e.g., [41]), propositional logic (e.g., [21]), progressive instantiation schema [8], among others. The interested reader is referred to Gupta [9], O'Keefe and O'Leary [22], and O'Leary [24] for an extended overview of literature on verification and validation of knowledge bases.…”

An Expert System to Assist the Diagnosis of Ischemic Heart Disease

“…Therefore, traditional diagnosis by the physician lacks the reliability in two ways: a) uncertainty issues in reading the signs and symptoms, and b) difficulty to handle multiple signs and symptoms simultaneously as a human agent. Fuzzy expert systems can be considered for IHD diagnosis; however they are not able to address all types of uncertainty, especially ignorance, incompleteness and ignorance in fuzziness [4]. Such uncertainties were addressed by using a Belief Rule Base (BRB) in the literature in assessing clinical asthma suspicion [3].…”