Inexact Reasoning Research Articles

A fuzzy expert system shell using both exact and inexact reasoning

This paper presents a comprehensive expert system shell which can deal with both exact and inexact reasoning. A prototype of this proposed shell, code named as SYSTEM Z-IIe, has been implemented successfully. It is a rule-based system which employs fuzzy logic and numbers for its reasoning. Two basic inexact concepts, fuzziness and uncertainty, are both used and distinct from each other clearly in the system. Moreover, these two concepts have been built into two levels for inexact reasoning, i.e. the level of the rules and facts, and the level of the values of the objects of these rules and facts. Other features of Z-IIe include multiple fuzzy propositions in rules and dual fact input mechanisms. It also allows any combinations of fuzzy and normal terms and uncertainties. Fuzzy numeric comparison logic control is also available for the rules and facts. Its natural language interface which uses English with restricted syntax improves the efficiency of knowledge engineering. Z-IIe is also coupled to a Database Management System for supplying facts from existing databases if appropriate. All these features can be combined to build very powerful expert systems and are illustrated by an example.

A conceptual model for inexact reasoning in rule-based systems

Most expert knowledge is ill-defined and heuristic. Therefore, many present-day rule-based expert systems include a mechanism for modeling and manipulating imprecise knowledge. For a long time, probability theory has been the primary quantitative approach for handling uncertainty. Other (mathematical) models of uncertainty have been proposed during the last decade, several of which depart from probability theory. In this paper, so-called inference networks are introduced to demonstrate the application of such a model for inexact reasoning in a rule-based top-down reasoning expert system. This approach enables the formulation of a conceptual model for inexact reasoning in rule-based systems. This conceptual model is used to show some inadequacies in the certainty factor model, a model that has been proposed by the authors of the MYCIN system and that has actually been applied in expert systems. A syntactically correct reformulation of the certainty factor model is proposed, and this new formalism is used to discuss some of the model's properties.

On the applicability of maximum entropy to inexact reasoning

It is shown that under a certain interpretation of belief maximum entropy arises naturally in inexact reasoning. Practical and theoretical consequences of this method are then discussed.

Parallel rule-based fuzzy inference on mesh-connected systolic arrays

The authors describe a scheme for performing fuzzy-reasoning and fuzzy-inference operations in parallel on mesh-connected array processors such as the geometric arithmetic parallel processor (GAPP) and the advanced systolic array processor (ASAP). The reasoning mechanism is a general, fuzzy, rule-based inference network. The array is configured into stages, and the scheme operates in a pipeline configuration, where different sets of data move sequentially between the stages. The authors investigated the tradeoff between space and time complexity of the proposed technique and achieved a fast, space-efficient implementation of the approach. Before presenting the proposed system, they review briefly the concepts of inexact reasoning, fuzzy inference, and mesh-connected fine-grain parallel computer architecture.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

An application of possibility theory to object recognition

An application is presented of possibility theory to object recognition. The object recognition problem is treated as the process of classifying an unknown object in a taxonomy of models. Classification embraces a high range of applications which have noticeable common characteristics like a finite set of solutions but in return, inexact reasoning, a deep gap between input data and solutions, incomplete and contradictory data. How to deal with these characteristics is described in a system called Classic. Its main features are, an object oriented knowledge representation and fuzzy pattern matching procedures, issued from possibility theory.

The development of an expert computer system on medical consulation

In order to master fuzziness and uncertainty in solving human problems, an expert system shell SYSTEM Z-II which can handle both exact and inexact reasoning has been successfully developed. This paper describes how the Z-II system shell works in building a medical consultation expert system which accepts fuzzy human concepts and uncertain factors. This expert system ABVAB in the domain of “Abnormal Vaginal Bleeding” is described to demonstrate the power and flexibility of such system. The methods of approach in the development and testing of this system are presented together with the results.

Fuzzy concepts in expert systems

The authors present a comprehensive expert-system building tool, called System Z-II, that can deal with exact, fuzzy (or inexact), and combined reasoning, allowing fuzzy and normal terms to be freely mixed in the rules and facts of an expert system. This fully implemented tool has been used to build several expert systems in the fields of student curriculum advisement, medical diagnosis, psychoanalysis, and risk analysis. System Z-II is a rule-based system that uses fuzzy logic and fuzzy numbers for its inexact reasoning. It uses two basic inexact concepts, fuzziness and uncertainty, which are distinct from each other in the system. >

Knod—a knowledge based approach for process design

Process design is a multiagent and distributed problem solving activity that can be significantly enhanced by the advent of expert system technology. Although certain types of expert systems can be built in a straightforward fashion, to build knowledge-based systems that will act as intelligent aids in process design is a difficult task. Problems may range from a lack of expressive power in the knowledge representation language to isolation from domain-specific sources of information and computer programs. KNOD is an expert shell in a personal computer that acts as a front end to a modular process simulator running in a remote VAX computer. The shell has been written in LISP. It is a frame-based, object-oriented system. Included are an OPS language and an inexact reasoning facility. It has been used as a research vehicle to test the encoding of heuristic knowledge and strategies oriented to produce the automatic design and simulation of process structures.

Evidence accumulation & flow of control

A fundamental goal of computer vision is the development of systems capable of carrying out scene interpretation while taking into account all the available knowledge. In this article, we focus on how the interpretation task can be aided by the expected scene information (such as map knowledge), which, in most cases, would not be in registration with the perceived scene. The proposed approach is applicable to the interpretation of scenes with three-dimensional structures as long as it is possible to generate the equivalent two-dimensional orthogonal or perspective projections of the structures in the expected scene. The system is implemented as a two-panel, six-level blackboard and uses the Dempster-Shafer formalism to accomplish inexact reasoning in a hierarchical space. Inexact reasoning involves exploiting, at different levels of abstraction, any internal geometric consistencies in the data and between the data and the expected scene. As they are discovered, these consistencies are used to update the system's belief in associating a data element with a particular entity from the expected scene.

On monotone chaining procedures of the CF type

We study procedures in which revised estimates of variables are iteratively computed, from initially given estimates, via use of monotone functions. Under broad hypotheses, we show how to obtain the limit values, when these exist, by solving a single mixed integer program. This result aids the computation of ‘measures of belief’ in procedures based on inexact reasoning, such as occur in some ‘expert systems’.

A Computer Program which Uses an Expert Systems Approach to Identifying Minerals

This paper describes a mineral identification program which utilises a shell system for creating expert systems of a classification nature. It has been specifically tailored to three subdomains, these being identification of minerals in: hand specimens, thin sections of rocks, and polished sections of rocks. The program has facilities for building new expert systems and amending existing expert systems. The expert systems created use inexact reasoning, and thus can handle both missing and inaccurate data. Explanation and help facilities are also available.

Oases: An Expert System for Operations Analysis the System for Cause Analysis

An application of knowledge-based systems in the field of business decisionmaking is presented. The expert's reasoning processes and problem-solving techniques in the operations analysis domain are the primary focus, and OASES, an operations analysis expert system, is designed to play the role of an intelligent assistant and to aid management in diagnosing problems in production processes, such as automobile assembly or textile manufacturing. The system combines forward and backward inferencing mechanisms to interact with users in a mixed initiative format. The knowledge base is designed as a partitioned rule base, and the inference engine uses the Dempster-Shafer scheme for inexact reasoning.

A model of inexact reasoning in administration

This paper suggests a model for understanding the character of managerial intuition and offers experimental evidence lending support to it .Verbal protocols were used to examine the information processing strategies used by subjects in reaching a decision. Subjects were divided into two groups, experts and students. It is proposed that (a) decision making by successful executives results not so much from rigorous or exact analysis as it does from the way in which information is organized in the mind of the executive, (b) the dichotomy of decision making styles into rational and intuitive is misleading, and (c) decision making styles change over time. A better integration of research in administrative decision behaviour with the methodology and theory of cognitive science is advocated.

Management of inexact reasoning and uncertainty in expert systems

Management of inexact reasoning and uncertainty in expert systems

Knowledge based expert systems for medical diagnosis

Knowledge based expert systems' have been developed in the last decade for many different applications by adopting artificial intelligence techniques. The paper discusses the main characteristics of the expert systems devoted to medical diagnosis (knowledge representation, explanation capability, inexact reasoning) and addresses some of the limitations (mainly system validation and knowledge acquisition). Finally the paper sketches the overall organization of an expert system devoted to the evaluation of liver function.

A model of inexact reasoning in medicine

Medical science often suffers from having so few data and so much imperfect knowledge that a rigorous probabilistic analysis, the ideal standard by which to judge the rationality of a physician's decision, is seldom possible. Physicians nevertheless seem to have developed an ill-defined mechanism for reaching decisions despite a lack of formal knowledge regarding the interrelationships of all the variables that they are considering. This report proposes a quantification scheme which attempts to model the inexact reasoning processes of medical experts. The numerical conventions provide what is essentially an approximation to conditional probability, but offer advantages over Bayesian analysis when they are utilized in a rule-based computer diagnostic system. One such system, a clinical consultation program named mycin, is described in the context of the proposed model of inexact reasoning.