Knowledge acquisition for fuzzy expert systems

Expert systems have been successfully applied to a wide variety of application domains. to achieve better performance, researchers have tried to employ fuzzy logic to the development of expert systems. However, as fuzzy rules and membership functions are difficult to define, most of the existing tools and environments for expert systems do not support fuzzy representation and reasoning. Thus, it is time-consuming to develop fuzzy expert systems. In this article we propose a new approach to elicit expertise and to generate knowledge bases for fuzzy expert systems. A knowledge acquisition system based upon the approach is also presented, which can help knowledge engineers to create, adjust, debug, and execute fuzzy expert systems. Some control techniques are employed in the knowledge acquisition system so that the concepts of fuzzy logic could be directly applied to conventional expert system shells; moreover, a graphic user interface is provided to facilitate the adjustment of membership functions and the display of outputs. the knowledge acquisition system has been integrated with a popular expert system shell, CLIPS, to offer a complete development environment for knowledge engineers. With the help of this environment, the development of fuzzy expert systems becomes much more convenient and efficient. © 1995 John Wiley & Sons, Inc.     

The chaining syllogism in fuzzy logic

We analyze the validity of the chaining syllogism in fuzzy systems, i.e., whether two fuzzy rules IF F, THEN G, and IF G, THEN H imply the rule IF F, THEN H. Conditions are given under which this basic deduction scheme holds. "If A is predicated of all B, and B of all C, A must necessarily be predicated of all C." ;-The chaining syllogism according to Aristotle's Prior Analytics.     

Uncertainty management in expert systems using fuzzy Petri nets

The paper aims at developing new techniques for uncertainty management in expert systems for two generic class of problems using fuzzy Petri nets that represent logical connectivity among a set of imprecise propositions. One class of problems deals with the computation of fuzzy belief of any proposition from the fuzzy beliefs of a set of independent initiating propositions in a given network. The other class of problems is concerned with the computation of steady-state fuzzy beliefs of the propositions embedded in the network, from their initial fuzzy beliefs through a process called belief revision. During belief revision, a fuzzy Petri net with cycles may exhibit “limit cycle behavior” of fuzzy beliefs for some propositions in the network. No decisions can be arrived at from a fuzzy Petri net with such behavior. To circumvent this problem, techniques have been developed for the detection and elimination of limit cycles. Further, an algorithm for selecting one evidence from each set of mutually inconsistent evidences, referred to as nonmonotonic reasoning, has also been presented in connection with the problems of belief revision. Finally, the concepts proposed for solving the problems of belief revision have been applied successfully for tackling imprecision, uncertainty, and nonmonotonicity of evidences in an illustrative expert system for criminal investigation     

Computer networks design using hybrid fuzzy expert systems

 Designing and configuring large computer networks to support a variety of applications and computational environments is difficult, as it not only requires highly specialized technical skills and knowledge, but also a deep understanding of a dynamic commercial market. Hybrid fuzzy expert systems integrate fuzzy expert systems and neural networks methods replacing classical hard decision methods and providing better performance than traditional techniques. In this paper, we present an integrated fuzzy expert system, machine learning, and neural networks approach to large structured computer networks design and evaluation. After presenting an overview of the system and the major research choices, we describe in detail the system's modules and present examples of its potential use.     

Extending FuzzyCLIPS for parallelizing data-dependent fuzzy expert systems

FuzzyCLIPS is a rule-based programming language and it is very suitable for developing fuzzy expert systems. However, it usually requires much longer execution time than algorithmic languages such as C and Java. To address this problem, we propose a parallel version of FuzzyCLIPS to parallelize the execution of a fuzzy expert system with data dependence on a cluster system. We have designed some extended parallel syntax following the original FuzzyCLIPS style. To simplify the programming model of parallel FuzzyCLIPS, we hide, as much as possible, the tasks of parallel processing from programmers and implement them in the inference engine by using MPI, the de facto standard for parallel programming for cluster systems. Furthermore, a load balancing function has been implemented in the inference engine to adapt to the heterogeneity of computing nodes. It will intelligently allocate different amounts of workload to different computing nodes according to the results of dynamic performance monitoring. The programmer only needs to invoke the function in the program for better load balancing. To verify our design and evaluate the performance, we have implemented a human resource website. Experimental results show that the proposed parallel FuzzyCLIPS can garner a superlinear speedup and provide a more reasonable response time.     

On the use of interval mathematics in fuzzy expert systems

Fuzzy expert systems attempt to model the cognitive processes of human experts. They currently accomplish this by capturing knowledge in the form of linguistic propositions. Real-world problems dictate the need to include mathematical knowledge as well. Pattern matching is a critical part of the inference procedure in expert systems. Matches are made between data clauses, premise clauses, and conclusion clauses, forming an inference chain. Preprocessing the clauses may generate intervals of real numbers which are compared in the fuzzy matching algorithm. These same intervals may be used in arithmetic expressions. the purpose of this article is to devise a method for incorporating arithmetic expressions into inference process of Fuzzy Expert Systems. Interval arithmetic is used to evaluate these expressions. Logical relations between intervals are analyzed using probability theory. © 1994 John Wiley & Sons, Inc.     

The utility of information and risk-taking fuzzy expert systems

In this article we employ utility theory to determine the new state of working memory, after a group of rules have been fired in parallel in a fuzzy expert system. This is argued to be analogous to using utility theory in economics to determine what best action to take in decision making under risk. A class of utility functions is described to compute the utility of information in working memory similar to computing the utility of wealth in economics. We discuss a memory update algorithm (fuzzy truth maintenance system) that will produce the unique undominated state of working memory after a group of rules have executed under the parallel mode of operation. the fuzzy expert system is called risk-averse when it uses this memory update algorithm. We call the system riskseeking, or risk-taking, when certain actions are allowed to operate outside the memory update algorithm. Experimenting with a risk-taking expert system is an exciting new idea which will exist in our new fuzzy expert system shell FESS II.     

A multilevel weighted fuzzy reasoning algorithm for expert systems

The applications of fuzzy production rules (FPR) are rather limited if the relative degree of importance of each proposition in the antecedent contributing to the consequent (i.e., the weight) is ignored or assumed to be equal. Unfortunately, this is the case for many existing FPR and most existing fuzzy expert system development shells or environments offer no such functionality for users to incorporate different weights in the antecedent of FPR. This paper proposes to assign a weight parameter to each proposition in the antecedent of a FPR and a new fuzzy production rule evaluation method (FPREM) which generalizes the traditional method by taking the weight factors into consideration is devised. Furthermore, a multilevel weighted fuzzy reasoning algorithm (MLWFRA) incorporating this new FPREM, which is based on the reachability and adjacent place characteristics of a fuzzy Petri net, is developed. The MLWFRA has the advantages that i) it offers multilevel reasoning capability; ii) it allows multiple conclusions to be drawn if they exist; iii) it offers a new fuzzy production rule evaluation method; and iv) it is capable of detecting cycle rules     

Analog and digital image enhancement using fuzzy expert systems

 Image enhancement is a field that is being used in various areas and disciplines. Advances in computers, microcontrollers and DSP boards have opened new horizons to digital image processing, and have opened many avenues to the design and implementation of new innovative techniques. This paper compares image enhancement via the modification of the probability density function of the gray levels with the new techniques that involves the use of knowledge-base (fuzzy expert) systems that are capable of mimicking the behavior of a human expert. A fuzzy expert system based software for image enhancement, called SmartPhotoLab has been introduced for the above purpose. Present address: A. El-Osery Dept. of Electrical Engineering, New Mexico Tech, Workman Center Rm. 247 801 Leroy place, Socorro, NM 87801 e-mail: elosery@ee.nmt.edu. This work was supported in parts by NASA grants no. NAG2–1196 and 2-1480.     

Some applications of fuzzy logic in rule–based expert systems

Fuzzy logic has been used as a means of interpreting vague, incomplete and even contradictory information into a compromised rule base in artificial intelligence such as machine decision–making. Within this context, fuzzy logic can be applied in the field of expert systems to provide additional flexibilities in constructing a working rule base: different experts' opinions can be incorporated into the same rule base, and each opinion can be modeled in a rather vague notion of human language. As some illustrative application examples, this paper describes how fuzzy logic can be used in expert systems. More precisely, it demonstrates the following applications: (i) a healthcare diagnostic system, (ii) an autofocus camera lens system and (iii) a financial decision system. For each application, basic rules are described, the calculation method is outlined and numerical simulation is provided. These applications demonstrate the suitability and performance of fuzzy logic in expert systems.     

Application of fuzzy expert systems in assessing operational risk of software

Risk is the potential for realization of undesirable consequences of an event. Operational risk of software is the likelihood of untoward events occurring during operations due to software failures. NASA IV&V Facility is an independent institution which conducts Independent Assessments for various NASA projects. Its responsibilities, among others, include the assessments of operational risks of software. In this study, we investigate Independent Assessments that are conducted very early in the software development life cycle.Existing risk assessment methods are largely based on checklists and analysis of a risk matrix, in which risk factors are scored according to their influence on the potential operational risk. These scores are then arithmetically aggregated into an overall risk score. However, only incomplete project information is available during the very early phases of the software life cycle, and thus, a quantitative method, such as a risk matrix, must make arbitrary assumptions to assess operational risk.We have developed a fuzzy expert system, called the Research Prototype Early Assessment System, to support Independent Assessments of projects during the very early phases of the software life cycle. Fuzzy logic provides a convenient way to represent linguistic variables, subjective probability, and ordinal categories. To represent risk, subjective probability is a better way than quantitative objective probability of failure. Furthermore, fuzzy severity categories are more credible than numeric scores. We illustrated how fuzzy expert systems can infer useful results by using the limited facts about a current project, and rules about software development. This approach can be extended to add planned IV&V level, history of past NASA projects, and rules from NASA experts.     

Optimization of fuzzy expert systems using genetic algorithms andneural networks

In this paper, fuzzy logic theory is used to build a specific decision-making system for heuristic search algorithms. Such algorithms are typically used for expert systems. To improve the performance of the overall system, a set of important parameters of the decision-making system is identified. Two optimization methods for the learning of the optimum parameters, namely genetic algorithms and gradient-descent techniques based on a neural network formulation of the problem, are used to obtain an improvement of the performance. The decision-making system and both optimization methods are tested on a target recognition system     

Rule acquiring expert controllers

A paradigm is developed for a controller to learn to control an environment by use of a benefit measure to judge the control. Rules are acquired that fire in a stimulus-response fashion for control, and rules continue to be acquired to adapt to an evolving environment. The model includes both knowledge acquisition and skill refinement through bottom-up (data driven) learning of the top-down control strategy. It is more flexible than hardware learning systems such as ADELINE or MADELINE. The controller model self-organizes by acquiring rules, and adapts by continuing to update its rules while controlling an external environment. It does this by judging the benefit of feedback due to the selected control rules and keeping counts in cells from which a rule function is generated     

Two techniques of sensitivity and uncertainty analysis of fuzzy expert systems

Problems characterized by qualitative uncertainty described by expert judgments can be addressed by the fuzzy logic modeling paradigm, structured within a so-called fuzzy expert system (FES) to handle and propagate the qualitative, linguistic assessments by the experts. Once constructed, the FES model should be verified to make sure that it represents correctly the experts’ knowledge. For FES verification, typically there is not enough data to support and compare directly the expert- and FES-inferred solutions. Thus, there is the necessity to develop indirect methods for determining whether the expert system model provides a proper representation of the expert knowledge. A possible way to proceed is to examine the importance of the different input factors in determining the output of the FES model and to verify whether it is in agreement with the expert conceptualization of the model. In this view, two sensitivity and uncertainty analysis techniques applicable to generic FES models are proposed in this paper with the objective of providing appropriate tools of verification in support of the experts in the FES design phase. To analyze the insights gained by using the proposed techniques, a case study concerning a FES developed in the field of human reliability analysis has been considered.     

An approach to expert recommendation based on fuzzy linguistic method and fuzzy text classification in knowledge management systems

Since organizational tacit knowledge such as know-how and experiences usually resides in the owner’s brain, consulting the expert is an effective and efficient way to utilize this type of knowledge. However, users are no longer able to effectively find the appropriate experts in the knowledge management system due to the complexity and diversity of the expertise and the knowledge needs. In this paper, an approach to expert recommendation is proposed to assist the user to find the required experts. The method adopts the fuzzy linguistic method to construct the expert profile, that is, to model expert’s expertise. In addition, the fuzzy text classifier is used to get the relevant degree of the document to each knowledge area when the document is registered, which is the base of the following user profile construction. Then, the user profile consisting of the time and the relevance factors of the rated documents is constructed to derive the overall knowledge needs level of the user. Consequently, the expert that fulfills the knowledge needs most is recommended based on the similarity between the derived expert profile and the user profile. The developed prototype system, “knowledge management system in aircraft industry company”, is introduced and the experimental results show the proposed approach is feasible and effective.     

Restored fuzzy measures in expert decision-making

The paper deals with problems of fuzzy measure restoration from insufficient data on a finite set. The proposed approach is constructed in the class of second order Choquet capacities [Fuzzy Sets and Systems 31 (1989) 23; Reports of the Enlarged Session of I. Vekua Inst. Appl. Math. 14 (1999) 31; Ann. Inst. Fourier 5 (1953) 131] when “the fuzzy weights” of singletons are known. This essentially concerns certain frequency distributions, where the nature of additivity is doubtful because of the fuzzy nature of data distribution. This is the indisputable condition for the introduction of a fuzzy measure, but the insufficient one for its construction.Measures of specificity, indices of uncertainty and estimators of approximations are calculated. Some approximation properties are proved. Using the approach of A. Kaufman's experton theory [Les expertons, Hermes, Paris, 1987], the unique fuzzy subset (fuzzy object) is constructed from the associated probabilities [Fuzzy Sets and Systems 31 (1989) 23] of the restored fuzzy measure.The application of the constructed method is illustrated by an example of the processing of certain insufficient statistical data of the Georgian language. As a result, it becomes possible to derive estimates for the Georgian language on the whole.     

A fuzzy expert database system

Fuzzy concepts always exist in much of human reasoning as well as decision making. This paper presents a fuzzy expert database system which is an integration of a fuzzy expert system building tool called SYSTEM Z-II and a database management system called Rdb/VMS. This system is able to extract fuzzy data and terms stored in a database and used in the fuzzy reasoning in an expert system. It can also retrieve information by fuzzy database-queries which are generated by the expert system automatically. Many expert systems in different domain areas such as decision making can be constructed. Sample applications are described to demonstrate the flexibility and power of this system. The fuzzy query language defined and used in the system can also be used independently as a fuzzy enquiry tool in database applications.     

HEARTDROID—Rule engine for mobile and context‐aware expert systems

Building mobile context‐aware systems is inherently complex and non‐trivial task. It consists of several phases starting from acquisition of context, through modeling to execution of contextual models. Today, such systems are mostly implemented on mobile platforms, that introduce specific requirements, such as intelligibility, robustness, privacy, and efficiency. Over the last decade, along with the rapid development of mobile industry, many approaches were developed that unevenly support these requirements. This is mainly caused by the fact that current modelling and reasoning methods are not crafted to operate in mobile environments. We argue that the use of rule‐based reasoning tailored to mobile environments is an optimal solution. Rules are based on symbolic knowledge representation, as such they meet the general tendency to enforce understandability, intelligibility, and controllability of artificial intelligence software, as stated in the recent European Union General Data Protection Regulation. To this goal, we introduce a lightweight rule engine dedicated for Android platform called HEARTDROID. It executes models in the HMR+ rule language that are capable of expressing uncertainty of knowledge, capturing dynamics of mobile environment and provide high level of intelligibility. We present a qualitative and quantitative comparison of HEARTDROID with the most popular rule engines available.     

Belief updating in a fuzzy expert system

We argue that with the addition of a rule-ranking procedure, our fuzzy expert system FLOPS can correct previous conclusions given new, possibly conflicting, information.     

Fractals,fuzzy logic and expert systems to assist in the construction of musical pieces

The authors present a suite to assist in the creation of musical pieces, whose foundation lies on fractals, fuzzy logic and expert systems. Even though algorithmic music has been explored, some gaps still exist. The favored approach has consisted in mapping numbers to notes to create appealing pieces. This, we contend, is a necessary but not a sufficient condition. Our suite, besides the necessary mapping, possesses the following advantages. First, it is possible to define notes, tempos, and notes durations. Notes evolve according to the selected fractal. Tempos and durations can remain fixed or they also can follow a fractal. Second, it is possible to translate the resultant fractal notes into notes belonging to a musical scale. This is done by firing appropriate rules in a rule base. Third, interpretation templates are provided. Also, melodies or harmonies are available. The suite currently contains several known fractal systems, and we also proposed one dynamical, recursive computation based on Mamdani fuzzy rule bases. The suite we present helps promoting and monitoring the creative process of composing musical scores. The actual implementation of the suite was done on the Java language.