Software Integration of Real-Time Expert Systems

A fundamental shift in the preferred approach to building applied artificial intelligence (AI) systems has taken place since the late 1960s. Previous work focused on the construction of general-purpose intelligent systems; the emphasis was on powerful inference methods that could function efficiently even when the available domain-specific knowledge was relatively meager. Today the emphasis is on the role of specific and detailed knowledge, rather than on reasoning methods. The first successful application of this method, which goes by the name of knowledge-based or expert-system research, was the DENDRAL program at Stanford, a long-term collaboration between chemists and computer scientists for automating the determination of molecular structure from empirical formulas and mass spectral data. The key idea is that knowledge is power, for experts, be they human or machine, are often those who know more facts and heuristics about a domain than lesser problem solvers. The task of building an expert system, therefore, is predominantly one of “teaching” a system enough of these facts and heuristics to enable it to perform competently in a particular problem-solving context. Such a collection of facts and heuristics is commonly called a knowledge base. Knowledge-based systems are still dependent on inference methods that perform reasoning on the knowledge base, but experience has shown that simple inference methods like generate and test, backward-chaining, and forward-chaining are very effective in a wide variety of problem domains when they are coupled with powerful knowledge bases. If this methodology remains preeminent, then the task of constructing knowledge bases becomes the rate-limiting factor in expert-system development. Indeed, a major portion of the applied AI research in the last decade has been directed at developing techniques and tools for knowledge representation. We are now in the third generation of such efforts. The first generation was marked by the development of enhanced AI languages like Interlisp and PROLOG. The second generation saw the development of knowledge representation tools at AI research institutions; Stanford, for instance, produced EMYCIN, The Unit System, and MRS. The third generation is now producing fully supported commercial tools like KEE and S.1. Each generation has seen a substantial decrease in the amount of time needed to build significant expert systems. Ten years ago prototype systems commonly took on the order of two years to show proof of concept; today such systems are routinely built in a few months. Three basic methodologies—frames, rules, and logic—have emerged to support the complex task of storing human knowledge in an expert system. Each of the articles in this Special Section describes and illustrates one of these methodologies. “The Role of Frame-Based Representation in Reasoning,” by Richard Fikes and Tom Kehler, describes an object-centered view of knowledge representation, whereby all knowldge is partitioned into discrete structures (frames) having individual properties (slots). Frames can be used to represent broad concepts, classes of objects, or individual instances or components of objects. They are joined together in an inheritance hierarchy that provides for the transmission of common properties among the frames without multiple specification of those properties. The authors use the KEE knowledge representation and manipulation tool to illustrate the characteristics of frame-based representation for a variety of domain examples. They also show how frame-based systems can be used to incorporate a range of inference methods common to both logic and rule-based systems. "Rule-Based Systems,” by Frederick Hayes-Roth, chronicles the history and describes the implementation of production rules as a framework for knowledge representation. In essence, production rules use IF conditions THEN conclusions and IF conditions THEN actions structures to construct a knowledge base. The autor catalogs a wide range of applications for which this methodology has proved natural and (at least partially) successful for replicating intelligent behavior. The article also surveys some already-available computational tools for facilitating the construction of rule-based knowledge bases and discusses the inference methods (particularly backward- and forward-chaining) that are provided as part of these tools. The article concludes with a consideration of the future improvement and expansion of such tools. The third article, “Logic Programming, ” by Michael Genesereth and Matthew Ginsberg, provides a tutorial introduction to the formal method of programming by description in the predicate calculus. Unlike traditional programming, which emphasizes how computations are to be performed, logic programming focuses on the what of objects and their behavior. The article illustrates the ease with which incremental additions can be made to a logic-oriented knowledge base, as well as the automatic facilities for inference (through theorem proving) and explanation that result from such formal descriptions. A practical example of diagnosis of digital device malfunctions is used to show how significantand complex problems can be represented in the formalism. A note to the reader who may infer that the AI community is being split into competing camps by these three methodologies: Although each provides advantages in certain specific domains (logic where the domain can be readily axiomatized and where complete causal models are available, rules where most of the knowledge can be conveniently expressed as experiential heuristics, and frames where complex structural descriptions are necessary to adequately describe the domain), the current view is one of synthesis rather than exclusivity. Both logic and rule-based systems commonly incorporate frame-like structures to facilitate the representation of large amounts of factual information, and frame-based systems like KEE allow both production rules and predicate calculus statements to be stored within and activated from frames to do inference. The next generation of knowledge representation tools may even help users to select appropriate methodologies for each particular class of knowledge, and then automatically integrate the various methodologies so selected into a consistent framework for knowledge.

Ameliorating the Pregnant Man problem: a verification tool for personal computer based expert systems

Expert systems are built to solve a wide range of problems in domains such as medicine, science, engineering, manufacturing, education, and many other fields. A Rule-Based Expert System has the following components: the knowledge base, the inference engine, and a user interface that communicates with users. A Decision Tree is a hierarchical data structure consisting of nodes, which store information or knowledge, and edges, which connect the nodes. In this paper, we discuss that the inference or reasoning process of Expert System can be easier to identify by a decision tree structure through a forward reasoning inference or a backward reasoning inference. Forward reasoning inference is data-driven; its reasons for facts to find out what solutions (or conclusions) follow from the facts. However, backward reasoning inference is goal-driven, which needs to find facts that support the hypothesis. A small animal classification example will be discussed. For learning expert system programming effectively, we introduce two software tools and discuss their advantages. Firstly, CLIPS (C Language Integrated Production System) is discussed. CLIPS was designed using C programming language at the NASA/Johnson Space Center with the specific purpose of providing high portability, low cost, and easy integration with external systems. Secondly, an interactive logic programming tool, Visirule is discussed; VisiRule was offered by LPA, a small, private UK company specializing in practical Artificial Intelligence. Finally, we propose a Rubric for Outcome Assessments for learning expert system programming with decision Trees for anyone who is interested in practical expert system programming.

Artificial intelligence (AI) techniques using sophisticated computer software are increasingly being used in the analysis of biological systems and for the design of biological models. The term artificial intelligence refers to machine-based processes that are functionally equivalent to human cognitive tasks for analyzing data in the domain for which they were designed. They are considered intelligent because of human-like reasoning processes. The expert system and the artificial neural network are two examples of such techniques (1). We are in the process of adapting these new computer information processing techniques into a knowledge-based modelling system which will be used to selectively search, analyze, retrieve and display thermoregulatory data in response to operator requests. We have developed a prototype system in which a rule-based expert system integrates its knowledge base with our accumulated historical database of experimentally obtained information on thermoregulation to provide new retrieval and analysis capabilities which will be useful for both research and teaching. This enables you to address otherwise unmanageable problems. Heuristics are often used. These types of systems are able to work with uncertainty, reason about what is unknown, and handle multivalued information with multiple conclusions. They are very effective at handling the problems that they were designed to solve, and are excellent for situations in which many people require access to a body of knowledge. Expert systems or neural networks when used alone are not optimal. The deficits in each can be filled by designing an integrated hybrid system in which neural networks are embedded within the rule based expert system and so both synergistically fill the gaps in each others technology.

A rule-based expert system designed to locate the faults in a distribution system is presented. Distribution system component data and network topology are stored in the database. A set of heuristic rules is compiled from the dispatcher's experience and imbedded in the rule base. To locate distribution system faults, an inference engine is developed to perform deductive reasoning on the rules in the knowledge base. The inference engine comprises three major parts: the dynamic searching method, the backtracking approach, and the set intersection operation. The expert system is implemented on a personal computer using the artificial intelligence language Prolog. To demonstrate the effectiveness of the proposed approach, the expert system has been used to locate faults in a real underground distribution system within the service area of the Taipei City District Office of the Taiwan Power Company. It is found that the expert system can identify fault location very efficiently. Therefore, it can serve as a valuable tool to help distribution system dispatchers determine fault locations.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Rule-based systems are relatively easier to explain to end users because rules can be expressed as if/else conditions. Indeed, at times there are multiple if/else conditions. Expert systems are deterministic in nature and very accurate, because they are driven by well-established rules and conditions. The need for explainability arises when multiple rules either conflict or are combined in such a way that it is difficult to interpret. In the field of artificial intelligence, an expert system is a program designed to simulate a real decision making process. Expert systems are designed to solve complex problems by simple reasoning and are represented as if/else/then conditions. Expert systems are designed to act like an inference engine. The role of an inference engine is to make decisions. Sometimes expert systems are simple, like a decision tree, and sometimes expert systems are very complex. Complex expert systems require a knowledge base. Sometimes these knowledge bases are an ontology, which is a form of a body of knowledge, through which a particular rule can be framed and inferencing can be done.

Development of a prototype expert system for identification and control of insect pests

Expert systems have now proven their value in a variety of enterprises ranging from medicine to geology. Small expert systems are at work in numerous commercial and industrial sites, but large systems are still rare. Besides the difficulties inherent in the collection of useful and correct domain knowledge, large knowledge bases present special problems in data management. These problems often cause a good pilot system to founder when expanded to an operational size.This paper addresses the difficulties of efficiently handling large knowledge bases. The methods discussed result from viewing a knowledge base as a database and analyzing the knowledge as one would analyze a database application, using Chen's Entity-Relationship (E-R) Diagrams [CHEN76] to build a relational database representation. Besides providing a different perspective on the management of knowledge bases, such a method facilitates the use of a commercial DBMS for this task.Following these ideas the simple assertions of the knowledge base are viewed as one entity type and the rules themselves as another, with IF and THEN being separate relationships between these two types. This decomposition is reasonable for capturing the knowledge represented by the rules and leads to a relational description which is considerably different than usual rule representations. More precisely, the E-R diagram of Figure 1 is taken as the basis of the model, where the rectangles represent entities and the ellipses represent relationships between the entities connected to that ellipse. The unboxed words such as rule# are attributes of the associated entities and correspond to the information to be saved in the database.The E-R diagram in Figure 1 can be converted to the relational database tables shown in Figure 2 where rule# is a name or a number that identifies the rule, if_part is a simple assertion Ai (antecedent), then_part is a simple assertion Ci (conclusion), and conf is a number representing the confidence in that deduction.This results in a very different data organization from the usual collection of knowledge found in rule-based systems. Beyond that analysis, very specific implementation techniques are suggested for knowledge bases, using both standard relational data bases and simpler file-handling techniques such as those available in standard programming languages. Caution must be taken, however, before simply trying to turn knowledge base management over to a commercial DBMS, since the behavior of “queries” within a rule base is very different from that of ordinary database queries. Comparisons are made to such recent works as those of Deering, Debanne, and Bouzegoub which suggest a variety of implementations for knowledge bases.Often in the modeling of a domain, one discovers a hierarchical relation among the basic concepts, vocabulary, or objects of study. Such an IsA or AKindOf hierarchy can be used to create a segmentation of the knowledge base resulting in significant efficiency improvements within the system. This possibility is explored in some detail.Throughout this work, emphasis is given to dynamic rule bases which are subject to frequent changes and which, therefore, require a generalized inference engine and flexible data structures. Often due to execution speed requirements some domain knowledge is encoded as procedures within the inference engine. Although this may improve the efficiency of the system, it undermines the generality and flexibility that are also required. This paper investigates a promising method for incorporating such procedural inclusions within the general knowledge base structure. The approach developed allows the absorption of changes without restructuring of the knowledge base or modifications to the inference engine, and maintains system transparency, simplicity, and expandability.

SASHA is a model-driven application shell which generates expert systems for fault isolation and repair in complex electronic systems. SASHA incorporates an expert system shell and a knowledge acquisition tool into a unique mechanism that elicits data directly from the domain expert and outputs a rule-based diagnostic expert system.SASHA employs causal-model and rule-based representations. It uses a causal model to identify the structure, behavior and relation of a domain object. It uses rules to direct the diagnostic problem solver. SASHA's initial knowledge base consists of a domain independent generalized model of the system under analysis. This underlying model drives knowledge acquisition and the consequent generation of an expert system. SASHA introduces a new concept of Pattern Rules, which are a generalized skeleton of the diagnostic problem solver. During diagnosis, when applied to the domain specific Structural Knowledge base, Pattern Rules dynamically instantiate the actual domain rules. Based on the Pattern Rules notion, three types of rule base organization are proposed and analyzed: Fully Patternized Organization (FPO), Partly Patternized Organization (PPO) and Customized Domain-specific Organization (CDO).

Introduction. Expert witness reports, prepared with the aim of quantifying fault rates among parties, play an important role in a court's final decision. However, conflicting fault rates assigned by different expert witness boards lead to iterative objections raised by the related parties. This unfavorable situation mainly originates due to the subjectivity of expert judgments and unavailability of objective information about the causes of accidents. As a solution to this shortcoming, an expert system based on a rule-based system was developed for the quantification of fault rates in construction fall accidents. The aim of developing DsSafe is decreasing the subjectivity inherent in expert witness reports. Methodology. Eighty-four inspection reports prepared by the official and authorized inspectors were examined and root causes of construction fall accidents in Turkey were identified. Using this information, an evaluation form was designed and submitted to the experts. Experts were asked to evaluate the importance level of the factors that govern fall accidents and determine the fault rates under different scenarios. Based on expert judgments, a rule-based expert system was developed. The accuracy and reliability of DsSafe were tested with real data as obtained from finalized court cases. Result. DsSafe gives satisfactory results.

A rule-based expert system has been developed and used into a robotic planning system with collision-avoidance. The world model is represented as the knowledge data and stored in the knowledge base with rules. The expert system can find a collision-avoidance path. Otherwise, it shows the collision secton in which the moved object is blocked. Several examples for the different objects to pass through the different channels and to find a collision-free path have been tested. The simulating results of the planner give out the planning sequence or blocked section(s). The output information is useful for making decision of the robot motion and modifying the technological parameters of the world.

This eight-volume report presents guidelines for performing verification and validation (V&V) on Artificial Intelligence (Al) systems with nuclear applications. The guidelines have much broader application than just expert systems; they are also applicable to object-oriented programming systems, rule-based systems, frame-based systems, model-based systems, neural nets, genetic algorithms, and conventional software systems. This is because many of the components of AI systems are implemented in conventional procedural programming languages, so there is no real distinction. The report examines the state of the art in verifying and validating expert systems. V&V methods traditionally applied to conventional software systems are evaluated for their applicability to expert systems. One hundred fifty-three conventional techniques are identified and evaluated. These methods are found to be useful for at least some of the components of expert systems, frame-based systems, and object-oriented systems. A taxonomy of 52 defect types and their delectability by the 153 methods is presented. With specific regard to expert systems, conventional V&V methods were found to apply well to all the components of the expert system with the exception of the knowledge base. The knowledge base requires extension of the existing methods. Several innovative static verification and validation methods for expert systems have been identified and are described here, including a method for checking the knowledge base {open_quotes}semantics{close_quotes} and a method for generating validation scenarios. Evaluation of some of these methods was performed both analytically and experimentally. A V&V methodology for expert systems is presented based on three factors: (1) a system`s judged need for V&V (based in turn on its complexity and degree of required integrity); (2) the life-cycle phase; and (3) the system component being tested.

Dengue fever is a virus spread by Aedes mosquitoes. The statistics from the Ministry of Health Malaysia shows that the dengue fever cases are still high in Malaysia even though the overall cases decrease in number compared to years before. In addition, dengue fever is often misunderstood with other fevers. Therefore, this project aims to provide an alternative way to address the misunderstanding by developing an expert system with the capability to diagnose dengue fever through a web application. This project uses a Rule-Based Expert System with both inference engines, forward and backward chaining with a combination of certainty factor in handling uncertainty data to determine whether the patients experience dengue fever or other fevers. Knowledge acquisition is carried out by obtaining data from literature reviews and an interview session with a doctor, who is an expert in diagnosing fever. The findings of this project show that the techniques of Rule-Based and Certainty Factor can produce reliable results to determine the types of fever with a degree of accuracy of 75% and provide a recommendation of proper treatment and preventive measures for the types of fever accordingly.

Maintaining the safety and health of its workers by conducting daily health checks. The medical examination is carried out by a doctor or nurse at work. Workers who were declared unhealthy were not allowed to work on that day, in several cities, so that inspection standards were maintained, a web-based system was implemented that implemented a rule-based expert system. Naive Bayes algorithm which is included as an algorithm for classification based on data mining and machine learning activities. The initial process of Naive Bayes begins by studying existing data (complete with labels) through the training process. The results of this training are actually the probability of an outcome based on the variables used. Naive Bayes was chosen because it can study the results of past examinations and use them to make the next decision. This is felt to be a solution to the problems of previous system deficiencies that require the experience and expertise of health workers who are not necessarily equal.

Combining a neural network with a rule-based expert system approach for short-term power load forecasting in Taiwan