Inexact Reasoning Research Articles

Novel three-way decision model in medical diagnosis based on inexact reasoning

According to the clinical symptoms, a patient exhibits and medical domain knowledge, determining the type of disease the patient has is essential for medical diagnosis. Uncertainty is the nature of medical diagnosis. The intuitionistic fuzzy set (IFS) is one of the effective tools to deal with uncertainty problems. In recent years, IFS has been widely used to deal with medical diagnosis problems. The existing mainstream approach is to rank the underlying diseases and then assign the first-ranked disease to the patient. These methods cannot deal with situations where patients have multiple diseases at the same time or no disease at all. In addition, these methods may generate unreasonable diagnostic results in some cases. Consider clinical symptoms as evidence that a patient has or does not have the disease. With the increase in clinical symptoms, physicians will increase their belief or disbelief that the patient has the disease. Therefore, based on the method of inexact reasoning, this paper proposed an inexact reasoning model in medical diagnosis, namely IRM-MD, which can rank the underlying diseases and avoid generating unreasonable diagnostic results. Furthermore, a three-way decision model for medical diagnosis based on IRM-MD, namely 3WDM-IRM, is proposed. This model can effectively deal with cases where patients have multiple conditions at the same time or none at all, and prevent missed diagnoses and misdiagnoses. Finally, the numerical experiment results show that (a)the results generated by the IRM-MD model are similar to those of other existing models, and (b)the 3WDM-IRM model can effectively identify whether a patient has a disease or not, and identify multiple diseases that the patient has at the same time.

Expert System Diagnose Disease in Poultry Using Certainty Factor Method

In this study, the authors conducted research to apply the Certainty Factor Method to Diagnose Diseases in Livestock and build applications in the application of Reviews These methods. Information technology is now a staple in developing a field. Along with the development of the times, proper information management is needed, so we need an Appropriate computational method. In this case the expert system method is one way to find out or identify diseases in poultry that have been infected with bacteria or viruses. A form of management information system specifically created to support a plan in making decisions to solve a case or problem. Artificial Intelligence (Artifical Intelligence) is one part of computer science that makes machines (computers) can do work as and as well as humans do. Expert System is one of the Artificial Intelligence techniques that attempts to adopt human knowledge into computers, so that computers can solve problems as is usually done by experts. Diagnosing disease in poultry is currently a problem that is still underestimated for most people, this is due to lack of knowledge or lack of information in terms of Overcoming and Preventing Contaminated, viruses or bacteria. Certainty Theory Theory was suggested by Shortlife and Buchanan in 1975 to a accommodate the uncertainty of thinking or inexact reasoning of an expert. Phrases like "maybe", "most likely", "almost Certain". To a accommodate this, Researchers conducted a certainty factor method to describe the level of expert confidence in problems in diseases Suffered by chickens already infected with dangerous viruses.

Hierarchically weighted rough-set genetic algorithm of rock slope stability analysis in the freeze–thaw mountains

The instability of rock slope in the freeze–thaw mountains has abrupt and uncertain characteristics, and its prediction accuracy often is very low. How to accurately estimate the stability of this kind of rock slope is an urgent problem to be solved. In this paper, we presented the evaluation method of rock slope stability in the freeze–thaw mountains coupling the hierarchy analysis, the rough-set theory and the genetic algorithm. Fifty highway rock slopes in Taishun county of Zhejiang province, China, were selected as the examples in the study. The main factors influencing rock slope stability were obtained using the analytic hierarchy process (AHP), and their corresponding weights were given. The evaluation rules were extracted from rock slope instability examples using the hierarchy analysis, the rough-set theory and the genetic algorithm. Then the approximate reasoning method was proposed using the method of inexact reasoning and default inference. The proposed method takes inexact reasoning and default reasoning to deal with incomplete match or absence of information. The results show as follows: it is reasonable and feasible to evaluate rock slope stability using the proposed method; using the proposed method, we can grasp the key factors influencing on rock slope stability, and the reasoning is flexible; the presented method can improve the intelligent prediction accuracy of rock slope instability in the freeze–thaw mountains. It remains to be further studied whether the method presented in this paper is suitable for the stability evaluation of other rock slopes.

Appling Fuzzy Logic for Optimization Formulation of Sapphire Precision Grinding

Fuzzy logic is a mathematical theory of inexact reasoning that allows modeling of the reasoning process of humans in linguistic terms, and it is an effective method approved to deal with the fuzzy objects in many areas of engineering application. The purpose of this paper is to explore the relationship between grinding process parameters and surface quality parameters of sapphire wafer by using fuzzy theory and to obtain optimization parameters to enhance the processing efficiency and higher the surface quality of the sapphire in ultra-precision grinding. This paper sets up the triangular fuzzy logic model between processing parameters and surface quality parameters for precision lapping of sapphire, and 2 groups of optimization parameters are selected. Finally, the experimental results verified that the optimization parameters of sapphire lapping obtained by using fuzzy logic which can improve the grinding efficiency and the surface quality of sapphire.

Research on Cow Epidemic Disease Diagnosis Based Certainty Factor

According to actual of cow epidemic disease diagnosis, bidirectional reasoning scheme based on the symptom both characterization and autopsy is proposed, which is based on inexact reasoning of certainty factor. The process of both construction the knowledge database for cow epidemic disease diagnosis and the inexact reasoning based on certainty factor are amplified. A web-based system on the platform of Tomcat+JSP+MySQL+DWR about the cow epidemic disease diagnosis is realized base on MVC. By testing, it is shown that the monitoring 21 kinds of cow epidemic disease in China can be accurately diagnosed, basically. It can be widely used in breeding aquatics village, breeding farm, raising households and so on. It will play a positive role to prevent and control the cow epidemic disease.

A risk-metric framework for enterprise risk management

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> We describe a risk-metric framework that supports enterprise risk management. At the core of the framework is the notion of a risk profile that provides risk measurement for risk elements. By providing a generic template in which metrics can be codified in terms of metric space operators, risk profiles can be used to construct a variety of risk measures for different business contexts. These measures can vary from conventional economic risk calculations to the kinds of metrics that are used by decision support systems, such as those supporting inexact reasoning and that are considered to closely match how humans combine information. </para>

Fuzzy knowledge- and data-based models of damage to reeds by grazing of Greylag Geese

This paper describes a fuzzy and neuro-fuzzy approach to modelling feeding intensity of Greylag Geese on reed. As a consequence of the presence of some non-measurable or random factors and the heterogeneity of reed and goose behaviour, the relationships between the model variables are often not well known and the data collected have a high degree of uncertainty. A fuzzy approach was selected which can be applied with vague knowledge and data of high uncertainty. Fuzzy logic can be used to handle inexact reasoning in knowledge-based models with fuzzy rules and fuzzy sets to handle uncertainty in data. The neural network technique was applied to develop the fuzzy data-based models. For training, several dataset combinations of three lakes in North Germany were used. The generalisation capability of these models was checked for other lakes. The performance of these models was compared with the results of the fuzzy knowledge-based model developed in the next step. The knowledge base of this model contains the Mamdani-type rules formulated by a domain expert. All models were implemented using the Fuzzy Logic Toolbox of MATLAB ®.

Expert Systems: Principles and Programming

Expert Systems: Principles and Programming Joseph C. Giarratano and Gary D. Riley Fourth Edition, Course Technology, Boston, MA, 2004 856 pages, $131.95 Language: English ISBN: 0534384471 Expert systems is one of the most successful, practical, and recognizable subsets of classical Artificial Intelligence. The ability to supply decisions or descision-making support in a specific domain has seen a vast application of expert systems in various fields such as healthcare, military, business, accounting, production, video games, and human resources. The theoretical and practical knowledge of expert systems is indispensable for a computer science, management information science or software engineering student. In its fourth edition, Expert Systems: Principles and Programming, as the title suggests, aims to be used as a complete textbook on this topic. The authors are respected authorities on expert systems, and were involved in the development of the popular CLIPS expert system tool which is dealt with thoroughly in this book. The book itself is divided into two major sections: the first six chapters deal with the theory of expert systems, the rationale behind their historical development and the current state of research; the next six sections are an introduction to CLIPS and how to use it to develop practical applications. The clear division between theory and practice serves to guide the student in choosing specific topics throughout the book. Each chapter ends with a set of problems and a useful bibliography. Appendix G provides a comprehensive list of software resources and will prove to be a very valuable asset to the student interested in exploring the practical aspects of expert systems as well as those who will be developing commercial applications incorporating expert systems. The first six chapters provide an indepth intoduction to expert systems, and deal with the representation of knowledge and methods of inference and reasoning. Each of these topics are introduced from scratch—for example, when dealing with knowledge representation, logic is described from its very basics, starting from propositional logic. The chapters on reasoning under uncertainty and inexact reasoning are very well constructed and are the most attractive feature of the book for me. Topics such as fuzzy logic and Dempster-Shafer theory are explained in good detail along with their practical significance. There is an objective flow through the chapters which helps to tie in the concepts and give an understanding on the progress of topics. Also, the disadvantages and pitfalls behind expert systems in general, and specific topics are well documented. The second section focusing on the CLIPS expert system tool is meant to be an aid in understanding and reinforcing the concepts of the first section, but does not require a thorough reading of the latter. CLIPS, developed in part by the authors at NASA, has become quite popular as a tool for studying expert system in many university courses as well as being used in several commercial and industrial applications. The expert system programming in CLIPS does not require much experience with programming and can be picked up rapidly thanks to its simple syntax and the helpful examples in the book. A new feature of the fourth edition is the introduction of COOL, the CLIPS Object-Oriented Language, which allows expert systems programmers to develop their systems in an object-oriented environment. The section is supplemented by a CD containing Windows and MacOS executables for CLIPS and reference guides—all of which can be downloaded from the Internet as well. Although the CLIPS examples deal with some problems of uncertainty in reasoning, there is no mention of using fuzzy logic or Dempster-Shafer theory in a practical setting—a serious disadvantage to the effectiveness of the book, considering the availability of tools such as FuzzyClips. The book itself is expensive—on the wrong side of a hundred dollars, but that seems to be the trend for academic books these days and shouldn't be a deterrent to anyone interested in the subject considering it has become one of the standard textbooks on expert systems. Raheel Ahmad Department of Computer Science Southern Illinois University Carbondale, IL 62901, USA

A fault diagnosis expert system for distribution substations

This paper presents a practical expert system for the diagnosis of various faults which may occur in distribution substations. The backward inexact reasoning process is applied for the fault section estimation using the knowledge of topology, the operation rules of protective devices, heuristic knowledge of well-trained operators, and instantaneous alarms. In this paper, the overall structure, detailed knowledge-base and the efficiency of general methodology based on topology are discussed. The proposed system has been tested in a local control center in Korea as a part of an intelligent guidance system for the SCADA operators.

An inductive method for inexact reasoning

A notion of nondeterministic inference on inexact information is introduced. The model is characterized by a second order probability distribution on beliefs together with an updating procedure. Such nondeterministic inference processes naturally generate a corresponding deterministic process which essentially involves calculating the expected response of the nondeterministic process. Given that the number of propositional variables in the relevant language may vary, it is natural to consider hierarchies of inference processes, these being characterized by hierarchies of prior distributions. A number of logical principles are considered which in the presence of certain smoothness assumptions restrict the choice of hierarchies to hierarchies of symmetric Dirichlet priors where each prior is defined up to a parameter λ which is constant over the whole hierarchy. A principle of maximal expected dependency between propositional variables is then introduced which restricts λ to a particular value.

A model of inexact reasoning in mechanical design evaluation

This paper presents an inexact reasoning model based on the union of extended D-S theory and fuzzy theory, which is applied to evaluate mechanical designs. D-S theory is the main reasoning tool. To take account of the evaluation factors and their relationships, we adopt the inexact reasoning model (extended D-S theory) for top level evaluation, and multilevel fuzzy synthetic evaluation for bottom level evaluation. Conclusions of the bottom level evaluation are taken for intermediate data. Evidence reasoning is executed from bottom to top, to implement the evaluation of system design. In fuzzy synthetic evaluation, the mode M(·, ⊕) and evaluation factors with tree hierarchical structure have been utilized. When this inexact reasoning model was applied to mechanical design evaluation, the time complexity was only O( mn). It is much smaller than the time complexity O(2 2| ▪ ∗ |) produced by D-S theory only.

A fuzzy knowledge-based model of annual production of skylarks

This paper presents some first results obtained by modelling the annual production of skylarks in pure crop areas by means of fuzzy logic. The model has been developed using the Modelling Support System FLECO which employs fuzzy logic to handle inexact reasoning and fuzzy sets to handle uncertainty of data.

Cognitive-map-based decision analysis based on NPN logics

This paper presents a new method for cognitive-map-based decision analysis based on negative-positive-neutral (NPN) logics, where an efficient algorithm is proposed for performing interval-based inexact reasoning automatically. The algorithm performs inexact reasoning via constructing a sprouting tree. The time complexity of the proposed algorithm is O( nm), which n is the number of cognitive units in the cognitive map, m is the length of a closure path, and max( m) = 2 n.

A natural prior probability distribution derived from the propositional calculus

A σ-additive probability measure on the real interval [0, 1] is defined by considering the expected values of “randomly chosen” large formulae of the propositional calculus, where the propositional variables are treated as independent random variables on {0, 1} with expected value 1 2 . Although arising naturally from logical and/or cognitive considerations, this measure is extremely complex and displays certain formally pathological features, including infinite density at all points of a certain dense subset of [0, 1]. Certain variantsof the construction are also considered. The introduction includes an account of motivation for the study of such measures arising from a fundamental problem in inexact reasoning.

Heterogeneous transformation of uncertainties of propositions among inexact reasoning models

Cooperation between different expert systems within a heterogeneous distributed expert system is essentially based on the transformations between the reasoning models built into the structures of the involved systems. The general criteria for a transformation between heterogeneous inexact reasoning models have been formulated. Using an algebraic structure of semigroup (with individual unit elements) of inexact reasoning models, the criteria are natural and easily satisfied by homomorphic mapping as an implementation. The criteria presented are too weak to ensure the existence of a unique mapping between the models concerned, but they are strong enough to guarantee reasonable transformations. These general considerations are illustrated using the example of three inexact reasoning models used by EMYCIN, PROSPECTOR, and MYCIN, where the homomorphic transformations among them are constructed and discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

An intelligent control and decision approach for adaptive systems

An intelligent control and decision-making (ICD) approach that integrates expert systems technology with adaptive algorithms is presented. The controller parameters of adaptive systems can be determined using human expertise and knowledge, and they can also be adjusted based on active monitoring and identification. Decision-making, fine tuning and inexact reasoning provide the end-user and the control engineers with a natural and integrated methodology for use with intelligent control systems. Computer simulation results demonstrate the utility of the proposed technique that is an effective intelligent control and decision approach. The ICD system is implemented using a Lisp based expert system shell on an IBM PC.

Connectionist approaches to inexact reasoning and learning systems for executive and decision support: Conceptual design

Connectionist approaches to the representation and reasoning about problems addressed by executive and decision support systems are described. It is shown that analog object structures coupled with learning produce mechanisms for managerial problem diagnoses. These mechanisms are neural models with multiple-layer structures that support continuous input/output. Because a connectionist architecture is assumed, the prototype system is called the Connectionist Inexact Reasoning System or CIROS. Although uncharacteristic of neural architectures, an explanation capability is developed within CIROS and described herein. The primary contribution of CIROS is that it provides principles for design of inexact reasoning systems for managerial problem diagnosis.

A full theorem-prover under uncertainty

Prolog embodies an ordered input resolution inference mechanism, with a powerful unification procedure. However, Prolog is not a full theorem-prover, and does not contain an inexact reasoning mechanism. In this paper, it is shown how these capabilities can be combined in a Prolog environment. A Prolog meta-interpreter is used in an elegant and simple way for this purpose. The inexact reasoning mechanism is presented through the certainty factor model, but it is also discussed how other inexact reasoning models may be also implemented.

Traffic signal installation by the expert system using fuzzy set theory for inexact reasoning

To assist traffic managers in evaluating whether a traffic signal is needed at an intersection we have developed an Expert System using the fuzzy set theory. It is called “The Fuzzy Expert System.” This system can handle uncertain and vague input data such as changing traffic volume and pedestrian safety, whereas other expert systems using traditional logic cannot.

Diagnostic Expert Systems: Encoding Geological Knowledge for an Exploration Play Analysis

The U.S. Geological Survey is currently exploring the feasibility of applying diagnostic expert systems and knowledge-based acquisition techniques to the geologic analysis of sedimentary basins and petroleum exploration plays. This paper describes a unique approach to the design and application of a rule-based expert system to the analysis of exploration plays, a methodology commonly used to assess petroleum resources in a basin. This technique consists of using various geologic models and Monte Carlo simulation or probabilistic methods for analyzing geologic and reservoir conditions favorable to the occurrence of petroleum resources. Work is in progress to adapt the geologic model used in the play analysis to an expert-systems and knowledge base, the object being to capture the logic and reasoning used to characterize the geologic model and to evaluate the play. Expert systems techniques now being developed are capable of dealing with inexact reasoning or reasoning under uncertainty by incorporating degrees of uncertainty to deal effectively with incomplete, inferred, or interpretive data involved in the knowledge base. The goal of this study is to provide an embedded diagnostic expert-system approach for characterizing the geologic model to expand upon the play-analysis system. Such a system provides the geologist with capabilities to documentmore » major basin components, such as stratigraphy, structural geology, and sedimentation, and to analyze the traditional concepts of source, reservoir, and trapping mechanisms. This expert system will assist the geologist in compiling and interpreting the geologic and reservoir engineering data necessary for running the probabilistic methods to calculate the amount of probable petroleum resources within each play. Diagnostic expert-systems technology can provide challenging new tools in knowledge acquisition and data interpretation in petroleum geology.« less