A shell for signal interpretation expert systems

System monitoring and diagnosis through signal interpretation are important tasks in many engineering fields. Although monitoring or diagnosis expert systems help human experts a lot, it is not so easy to build a signal interpretation expert system. We have developed two expert systems for speech signal processing. The close analysis of the two systems revealed common functions and modules applicable to wide range of signal interpretation problems. On the basis of the experience, we have developed a shell for signal interpretation expert systems named SSI. In order to evaluate SSI, we also developed two expert systems by using it and obtained satisfactory results. This paper describes the design philosophy and architecture of SSI together with some examples.     

Distributed conflict resolution among cooperating expert systems

Abstract: Cooperating experts approach attempts to integrate and coordinate the activities of multiple specialised problem solvers that come together to solve complex tasks such as design, medical diagnosis, business management and so on. Due to the different goals, knowledge and viewpoints of agents, conflicts may arise at any phase of the problem-solving process. Managing diverse expertise requires well-organised models of conflict resolution. In this paper, a model for cooperating experts is described which openly supports multi-agent conflict detection and resolution. The model is based on the idea that each agent has its own conflict knowledge which is separated from its domain level knowledge, and each agent has its own conflict resolution knowledge which is not accessible and known by others. Furthermore, there are no globally known conflict resolution strategies. Each agent involved in a conflict chooses a resolution scheme according to its self interest. The model is described by using an example in the domain of office design and it is compared with other systems.     

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.     

An agent-based framework for cooperating expert systems in concurrent engineering

This paper describes an agent-based framework with a tandem integration architecture for cooperating distributed, heterogeneous, and autonomous concurrent engineering systems. The approach has been experimented with using the prototype AGENTS system. It is demonstrated that design tools can be dynamically connected and configured to construct highly complex systems on an incremental basis, designing and proving one system at a time without bothering the rest. A major advantage of the approach is the balance between the interoperability and the autonomy of individual systems.     

A tandem architecture for cooperating knowledge-based concurrent engineering systems

This paper proposes a tandem architecture for cooperating heterogeneous expert systems. Two levels of meta and working expert systems are involved. The working-level expert systems (W-ES), which may be implemented in their own computational environments and in private proof languages, are mainly for application computations. The meta-level expert systems (M-ES), using a common argument language, are mainly responsible for cooperation. The prototype AGENTS system is described for constructing M-ES. Interaction among W-ES has been transformed into two forms: communication between M-ES through ordinary AGENTS messages and communication between M-ES and the corresponding W-ES using the Deductive Inference Language (DIL). DIL predicates are provided for defining DIL: messages, actuators and converters for interpreting DIL queries and instantiating variables. By this approach, stand-alone capability of infividual systems is retained at the working-level and cooperation is achieved effectively with minimum embellishment at the meta-level.     

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]     

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.     

Left-forbidding cooperating distributed grammar systems

A left-forbidding grammar, introduced in this paper, is a context-free grammar, where a set of nonterminal symbols is attached to each context-free production. Such a production can rewrite a nonterminal provided that no symbol from the attached set occurs to the left of the rewritten nonterminal in the current sentential form. The present paper discusses cooperating distributed grammar systems with left-forbidding grammars as components and gives some new characterizations of language families of the Chomsky hierarchy. In addition, it also proves that twelve nonterminals are enough for cooperating distributed grammar systems working in the terminal derivation mode with two left-forbidding components (including erasing productions) to characterize the family of recursively enumerable languages.     

Teams in cooperating grammar systems

Abstract

We consider grammar systems in which several components are active at the same moment (a team of components is working). The power of such mechanisms is investigated and it is found that in many cases the team feature increases the generative capacity of grammar systems. In the so-called t-mode of derivation (a team works as much as it can) it is found that the team size does not induce an infinite hierarchy of languages. However, the family obtained in this case is a full abstract family of languages properly including ETOL.     

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.     

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.     

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 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 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 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.