A shell for cooperating expert systems

Abstract: This paper describes a shell for cooperating expert systems that has been developed at the University of Porto. The main goal of this shell is two-fold: to generate a community of cooperative knowledge-based systems and to develop several special reasoning techniques which can be used under a distributed and cooperative paradigm. UPShell is able to convert a set of generated intelligent systems (ISs) into a community of cooperative ISs. In this first version it is already possible to generate different intelligent systems which are able to run 'simultaneously' as separate Unix processes and, using a message-passing mechanism, to communicate among themselves. They can be set to pursue an overall goal in a cooperative way. Moreover, several tasks can be given to each IS to be solved simultaneously, and the IS can switch from task to task according to dynamic priorities reflecting the urgency attached to the specific sub-tasks that emerge. The shell described here may also be used to test, within a distributed environment, some time-bounded reasoning techniques that are presently being developed. The paper has three main parts: a general overview of the UPShell (Section 1); a tutorial explaining, by means of examples, how to use the package (Section 2); and, finally, some considerations on the reasoning techniques used and future improvements (Sections 3–5).     

An explanation shell for expert systems

We describe a shell for expert systems written in Prolog. The shell provides a consultation environment and a range of explanation capabilities. The design of the shell is modular, making it very easy to extend the shell with extra features required by a particular expert system. The novelty of the shell is twofold. Firstly, it has a uniform design consisting of an integrated collection of meta-interpreters. Secondly, there is a new approach for explaining 'why not,' when a query to the system fails.     

A structure for expert systems maintenance

An approach to the design of maintainable expert systems is presented. Central to this approach is a conceptual model in which the data and knowledge are both modelled as formal “items” in a uniform way. “Objects” are introduced as “item building” operators. The notion of the “decomposition” of items and objects provides the foundation for a single rule of normalization. This single rule applies to all items and objects, including knowledge items, and is a non-trivial generalization of the traditional normal forms for database. Coupling relationships represent the necessary maintenance paths in the conceptual model. A complete characterization of coupling relationships is given, and the value of normalization to the reduction of maintenance costs is discussed.     

A methodology for expert systems development

There are virtually no methods or techniques available for developing expert systems in law. Further, with the exception of the UK government's Central Computer and Telecommunications Agency project, GEMINI, [1] there has been no attempt to lay the foundations for an expert systems development methodology in general. This paper discusses a number of conventional software engineering methodologies and explains why the spiral model is most suited to the development of legal expert systems. This also happens to be the same model which was advocated by the results of the GEMINI project.[2]     

The role of expert systems in producing log interpretation software

Abstract: Log interpretation science is a controversial and rapidly changing domain. Designing interpretation models is a highly experimental process which involves trials with a computer program as an integral part of the design. Therefore conventional software engineering techniques, which require a complete specification of the problem before the program is written, are often not applicable or fail to produce high quality software. The development of expert systems has provided the techniques, tools, and capabilities to let us seek alternate methods to produce log interpretation software: exploratory programming environments and automatic programming systems. An exploratory programming environment combines the power of interactive graphics and programming tools to merge the design and programming tasks into a single process where model and program develop together. An automatic programming system will embody the knowledge of the programming process and of some log interpretation heuristics to produce log processing programs from interactive specifications expressed in familiar terms. These facilities will allow log interpretation model designers, who are non-computer specialists, to produce high quality software as the end result of a model design.     

Constructing and consulting the knowledge base of an expert systems shell

Abstract: This paper presents an expert system shell whose inference mechanism uses backward chaining. In particular the modules devoted to constructing and consulting the knowledge base are illustrated. The programming environment is based on the Arity-Prolog language, a popular Prolog dialect running on IBM PCs and compatibles.     

Basic algorithmic shell of onboard real-time advisory expert systems for operation situations typical for an object

The basic algorithmic shell for onboard real-time advisory expert systems for typical operation situations of anthropocentral objects is oriented on the formal model of the subject domain which includes the following ideas: general operation problems of an anthropocentral object, semantic networks of typical operation situations and problem subsituations in them. This system possesses two hierarchical levels in the knowledge base. On the first level, the production rules activate an adequate problem subsituation in real time. On the second level, problems of this subsituation are solved using the dynamic models of development of its fragments using the following inference mechanisms: multicriteria choice of a decision alternative, decision according to a precedent, decision based on an optimization problem, production rules. Upon development of an onboard real-time advisory expert system for a particular typical situation, the basic shell is filled with knowledge related to this typical situation with simultaneous rejection of unclaimed fragments. Upon program implementation of the algorithmic shell filled with knowledge, the shell is adapted to the onboard information environment of a given type of anthropocentral object and computational capabilities of the onboard computer system.     

A comparative analysis of methods for expert systems

Given the current widespread interest in expert systems, it is important to examine the relative advantages and disadvantages of the various methods used to build them. In this paper we compare three important approaches to building decision aids implemented as expert systems: Bayesian classification, rule-based deduction, and frame-based abduction. Our critical analysis is based on a survey of previous studies comparing different methods used to build expert systems as well as our own collective experience over the last five years. The relative strengths and weaknesses of the different approaches are analysed, and situations in which each method is easy or difficult to use are identified.     

Design for assembly: A challenge for expert systems

Anticipating the increasing use of new and emerging computer technology in product engineering, design for assembly will be achieved effectively by applying advanced data processing technology and data management methods creating an assembly knowledge base and decision support system to close the loop between design, process planning and actual assembly. This paper introduces DFA and describes a strategic and conceptual approach to realise such a decision support system leading to an expert system for Design for Assembly in the near future.     

Prototypical knowledge for expert systems

Knowledge of situations typically encountered in performing a task is an important and useful source of information for solving that task. This paper presents a system that uses a representation of prototypical knowledge to guide computer consultations, and to focus the application of production rules used to represent inferential knowledge in the domain. The explicit representation of control knowledge for each prototypical situation is also emphasized.     

A least exception logic shell for intelligent systems

In this article, we define an approach that is a significant step towards meeting the requirements for reasoning with incomplete and uncertain information. Least Exception Logic (LEL) handles both parametric and symbolic information and goes far toward satisfying the requirements for default conclusions, nonmonotonicity, truth maintenance, ordering the extensions, and practicability. No other single approach known to the authors addresses all these factors, and some of the more prominent approaches, such as default logic, Dempster-Shafer, assumption-based truth maintenance, and neural networks can be closely emulated through LEL. the heart of the approach is the decomposition of resolution into unification and solution, and performance of solution as an integer linear program (ILP). We describe a full-scale expert system shell for LEL. We indicate how knowledge is entered into the shell, how special predicates and operators can be used, how the inferencing process proceeds by including control, pattern matching, and ILP algorithms, and the software architecture. Several experiments are performed on the LEL shell in the final section of the article.     

A token-flow paradigm for verification of rule-based expert systems

This paper presents a novel approach to the verification of rule-based systems (RBSs). A graph structure, called the rule-dependency graph (RDG), is introduced to describe the dependency relationship among the rules of an RBS, in which each type of improper knowledge forms a specific topological structure. Knowledge verification is then performed by searching for such topological structures through a token-flow paradigm. An algorithm is provided, which automatically generates a minimally sufficient set of literals as test tokens in the detection procedure. The proposed scheme can be applied to rules of non-Horn clause form in both propositional and first-order logic, and restrictions imposed by other graph-based approaches can be avoided. Furthermore, explicit and potential anomalies of RBSs can be correctly found, and efficient run-time validation is made possible.     

A brief introduction to expert systems

Recent interest in the topic of expert systems has been enormous, and continues to grow. This allegedly new tool has been proposed for implementation in an incredibly diverse array of problems, including a host of problem types that are (or should be) of interest to the operations researcher. In this tutorial, we present a very brief overview of expert systems. In particular, we examine its relationship and usefulness to the operations research profession.     

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     

A statistical approach to knowledge acquisition for expert systems

Classical expert systems are rule based, depending on predicates expressed over attributes and their values. In the process of building expert systems, the attributes and constants used to interpret their values need to be specified. Standard techniques for doing this are drawn from psychology, for instance, interviewing and protocol analysis. This paper describes a statistical approach to deriving interpreting constants for given attributes. It is also possible to suggest the need for attributes beyond those given.The approach for selecting an interpreting constant is demonstrated by an example. The data to be fitted are first generated by selecting a representative collection of instances of the narrow decision addressed by a rule, then making a judgement for each instance, and defining an initial set of potentially explanatory attributes. A decision rule graph plots the judgements made against pairs of attributes. It reveals rules and key instances directly. It also shows when no rule is possible, thus suggesting the need for additional attributes. A study of a collection of seven rule based models shows that the attributes defined during the fitting process improved the fit of the final models to the judgements by twenty percent over models built with only the initial attributes.     

A probabilistic interpretation of the medical expert system CADIAG-2

CADIAG-2 is a well-known expert system aimed at providing support for medical diagnose in the field of internal medicine. CADIAG-2 consists of a knowledge base in the form of a set of IF-THEN rules that relate distinct medical entities, in this paper interpreted as conditional probabilistic statements, and an inference engine constructed upon methods of fuzzy set theory. The aim underlying this paper is the understanding of the logical structure of the inference in CADIAG-2. To that purpose, we provide a (probabilistic) logical formalisation of the inference of the system and check its adequacy with probabilistic logic.     

MAG-EX: A magnetics fabrication expert systems focusing expert systems technology on improving quality control

This paper describes the development and experiences involved with creating a prototype application for the use of expert systems technology in manufacturing, specifically in quality control. The company described in the paper is an electronics manufacturer specializing in power supplies and amplifiers for military and commercial applications. It currently relies heavily on traditional manufacturing information systems technology. Quality control must meet DoD and other regulations and directly affects the bottom-line. As a potential expert system project, management felt as though product quality and the company as a whole could benefit from strategic development in Quality Assurance. The paper discusses the process, the difficulties, and the derived benefits to quality control from the development of an expert system application. Further, the paper reports on additional training benefits derived from the explanation capability of the expert system.     

Building expert systems for repair domains

Abstract: This paper describes a conceptual framework for building expert systems in repair domains. A specific example of the use of this framework in building an expert system for a field service repair problem is given. The example system is designed to assist field service technicians in troubleshooting and repairing electronic systems at the board level. This system, a Field Service Advisor (Fieldserve), is designed (1) to diagnose single or multiple defects in electronic systems, (2) to guide a technician through appropriate repair procedures, (3) to verify diagnoses and monitor repair effectiveness and (4) to maintain records for subsequent evaluation and quality control.     

A method for putting strategic common sense into expert systems

Strategic common sense is defined as the heuristic considerations human experts often apply to decide which choice is the most opportune to make from a set of possible alternatives. A method for acquiring modeling, and representing human strategic common sense in diagnostic expert systems is presented. The preference parameter and preference criterion tools, which are used to define a basic level of strategic knowledge, are reviewed. Strategic common sense and its representation using general rules and metarules modeling are discussed. An interview with a physician who provided a medical case and the related solutions is summarized. It is shown that the solutions given by the physician match the conclusions obtained by applying the strategic common sense rules to the medical example