Second-generation Expert Systems Research Articles

Analysıs of Stages of Development, Current State and Prospects of the Expert Systems

The objects of the study are stages of development and modern state. In general terms, expert systems are knowledge-based systems. This paper focuses on the components and principles of expert systems. Expert systems are also described. The components of expert systems include knowledge base, logical impact mechanism, user interface and decision-making. In addition, this article describes the capabilities of expert systems. One challenge is to identify the future prospects of expert systems. The research examined the expert system and its significance. It also focuses on generations of expert systems. The first generation of expert systems includes systems created before 1990. This article discusses SAINT, DENDRAL and HEARSAY-1. The features of this expert systems are also discussed here. First-generation expert systems are research prototypes. As a result, the foundations of artificial intelligence were developed. Mostly first-generation expert systems were used as a passive assistant expert. The second generation of expert systems refers to systems created since 1990. Features of second-generation expert systems include dynamism, interactivity, and processing of disparate knowledge. Unlike first-generation expert systems, these systems are able to test the completeness of the knowledge base, to process fuzzy knowledge. Their main difference is the ability to integrate second-generation expert systems with existing systems. At the moment, statistical and dynamic expert systems are distinguished. This article describes the current status of both types. Here are also discussed the tools of statistical and dynamic expert systems. At the end, possible prospects of expert systems are received.

Special section on advances and applications of problem orientation

Early software development approaches focused on program code as paramount, paying little attention to any analysis or understanding of its generative real-world context and needs. More modern problem-oriented approaches view software development as problem solving, and use many of the rich toolset and techniques of the knowledge engineering (KE) area. ‘When one encounters a new situation (or makes a substantial change in one's view of the present problem) one selects from memory a structure called a Frame. This is a remembered framework to be adapted to fit reality by changing details as necessary’. ‘We intuitively believe that experienced problem solvers construct new systems by modifying known systems’. Both Problem Frames and Domain Theory use modelling as a means for problem understanding and representation. In this respect, they are akin to the Common KADS framework (Breuker & van de Velde, 2004) that provides a suite of re-usable knowledge models for expert systems development. According to Studer et al. (1998), modelling marks an important transition from first-generation to second-generation expert systems, as the modelling approach to knowledge engineering had taken over the knowledge transfer approach. Problem-oriented approaches to software development have two essential attributes. Firstly, they recognise the need to work iteratively between real-world problem and software solution to increase understanding: solutions are sometimes straw men, allowing the developer to reflect on the problem to understand it better. Indeed, the importance of this knowledge building process in design has been widely acknowledged (Alexander, 1979; Dasgupta, 1991). Secondly, they view software development as problem solving and knowledge reuse. This special section collects the best papers presented at the International Workshop on Advances and Applications of Problem Orientation, held in Cape Town, South Africa, in May 2010. These papers represent a snapshot of current research and practice in problem-oriented approaches to software development. In the first paper, Luigi Lavazza addresses the problem that during analysis and development user requirements are elicited and perceived at various levels of abstraction, by proposing a systematic approach to relating such diverse requirements through the establishment of requirements hierarchies. The work is situated within Problem Frames and applied to a comprehensive case study. The work is particularly relevant to addressing business problems in which a need arises to relate business goals to software requirements. In the second paper, Xinyu Wang and his colleagues present an approach that guides a business analyst to derive a set of problem frames from a given problem description. The approach contains three steps. In the first step, business process models are developed to capture the behavioural view of the problem context. In the second step, object analysis models are used to capture the structural view of the problem context. Together, these two views collectively and comprehensively capture the early context knowledge. These two types of model are then used in a third step to construct context diagrams and problem frames. Finally, Jelena Marincic and her colleagues address the problem of how to construct adequate problem models, the starting point of solution seeking, through methods that properly account for both formal and informal modelling decisions and steps. Their approach is located within an industrial setting and the development of embedded systems. Action research was applied in such a setting to help improve problem analysis and problem model validation. Collectively, this special section aims to give a flavour of current thinking in this important area of research and practice. We hope this special section will continue to stimulate the debate and discussion.

Workflow agents versus expert systems: Problem solving methods in work systems design

AbstractDuring the 1980s, a community of artificial intelligence researchers became interested in formalizing problem solving methods (PSMs) as part of an effort called “second-generation expert systems.” We provide an example of how we are applying second-generation expert systems concepts in an agent-based system for space flight operations, the orbital communications adapter mirroring system (OCAMS), which was developed in the Brahms multiagent framework. Brahms modeling language provides an ontology for simulating work practices, including groups, agents, activities, communications, movements, and geographic areas.Activitiesare a behavioral unit of analysis to be contrasted withtasks, a functional unit of analysis. Problem solving occurs in the context of activities in the service of tasks; appropriate PSMs depend on the context: which people/roles are participating, what tools are available, how the results will be evaluated, and so forth. A work practice simulation facilitates designing workflow tools that appropriately interact with the physical and organizational context in which work occurs. OCAMS was developed using a simulation-to-implementation methodology, in which a prototype workflow tool was embedded in a Brahms simulation of how people would use the tool. The reusable components in a workflow system like OCAMS include entire “problem solvers” (e.g., a planning subsystem), interoperability frameworks, and agents that inspect and change the world. Thus, a tool kit for building workflow tools requires more than a library of PSMs, which play a relatively small role in the overall multiagent, systems-integration architecture. Our research concern has shifted to situations that may arise that are outside the OCAMS' capability. In practical decision making, people must reflect on the validity of their models. As programs becoming actors in the workplace, we need to develop systems that help people to understand the limitations of the models that drive the automated operations, which means in part detecting when the formalizations in the system are inadequate.

Knowledge-Based System Explanation: The Ripple-Down Rules Alternative

The ability to provide explanations has been seen as a key feature of expert systems (ES) typically not offered by other types of computer systems. ES need to offer explanations because of imprecise domains and the use of heuristics. Verification is not enough. ES need to justify and be accountable. Explanation is seen as an important activity for knowledge-based systems as it satisfies the user's need to decide whether to accept or reject a recommendation. In this paper we review explanation in first-generation and second-generation ES. An alternative is offered to the main approaches which uses multiple classification ripple-down rules and challenges even the goals of explanation. Instead of trying to give explanations which provide a meaningful line of reasoning and which are tailored to suit the individual it may be just as useful to provide the user with sufficient information and browsing tools to develop their own line of reasoning. The type of information that can assist understanding is the context in which the recommendation applies (which is provided through the display of relevant cases and exception rule history) and the ability to explore an abstraction hierarchy of the rules using formal concept analysis. An explanation toolkit aimed at putting the user in control is described and evaluated in this paper.

KROL: a knowledge representation object language on top of Prolog

This paper presents a knowledge representation object language (KROL) on top of Prolog. KROL is aimed at providing the ability to develop second-generation expert systems. The main aspects of KROL include multi-paradigm knowledge representation (first-order predicate logic, objects, rules), inference mechanisms at different levels of granularity, explanation facility, object-oriented database management module, and user-friendly interface. KROL has sufficient expressive power to be used in applying demanding knowledge-based modeling methodologies, such as KADS and Generic Task, which are the major landmarks of the second-generation expert systems technology. Four successful agricultural expert systems have been developed in the last 6 years using KROL. To demonstrate the language capabilities, we present an example of disorder diagnosis.

An overview of MICKEY: A knowledge-based hardware-software codesign framework for microprocessor-based systems

We present the architecture of a second-generation expert system for the automated design of microprocessor-based systems. A novel feature is the integration of a device handbook knowledge base with a shallow expert system, to provide resilience and deep reasoning capability. The design tasks, knowledge sources, behaviour modelling scheme, behaviour mapping algorithms, and inter-layer communication are briefly described.

MARS: an aid for network restoration after a local disturbance

The MARS system has been designed to generate restoration plans in the case of a local disturbance, leading to the loss of power supply on a limited number of HV or EHV substations. A restoration plan is a sequence of discrete actions on the network. To construct these plans, the operators must take into account general restoration guidelines or local constraints, but most of the procedure relies on their experience. The aim of the MARS project is to provide the operator with an aid during these delicate phases of network monitoring. MARS is a second-generation expert system which takes into account man-machine cooperation. Organized around a blackboard architecture, it integrates planning knowledge sources containing the restoration expertise, a qualitative model to predict the results of the considered actions, and a quantitative model to verify the correctness of the plans.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Second Generation Diagnostic Expert Systems: Requirements, Architectures and Prospects

Abstract An introduction to second-generation expert systems is provided starting with a discussion of the limitations of first-generation systems. A few architectures proposed for the second-generation expert systems are reviewed, a tour on the expert systems approach to fault diagnostic/control system design is made, and some issues that must be addressed in future works are highlighted. A sketch of a deep expert system developed by the author’s group is also provided.

A Second-Generation Expert System for Computer-Aided Control System Design

Abstract The CACE-III expert system for designing series lead-lag compensators for single-input/single-output plants to satisfy three frequency-response specifications has been implemented using the Gold Works inference engine and expert-system development tools. The implementation makes use of the frames and user-interface features of Gold Works to provide an environment from which further extensions of the design approach or other design methods can be created in an orderly fashion. The control-analysis program Matrix x has been used to perform the required control-system modeling and analysis calculations at the request of the expert system The tools that have been developed for handling the varied interactions between the expert system and the numerical applications program are described. Also, a number of ways in which the present design system can be improved and expanded are presented

Expert systems as a tool for statistics: A review

The current state of using expert system (ES) techniques for statistics is expounded through the difficulties of defining the notion of statistical expert system (SES) and through several well-known examples. The main characteristics of statistical expertise, and particularly the important concept of strategy are emphasized. The current state of SESs is seen in the context of the global trend of the evolution of ES, i.e. within the growth of second generation expert system (SGESs). The knowledge base of these ESs represent an attempt to represent not only experts' knowledge but also the way experts acquire their expertise, thanks to the existence of an underlying model. We show through many examples how far SESs are and/or will be able to be SGESs.

What is a deep expert system? An analysis of the architectural requirements of second-generation expert systems

What is a deep expert system? An analysis of the architectural requirements of second-generation expert systems

KNOWLEDGE ACQUISITION TECHNIQUES AND SECOND-GENERATION EXPERT SYSTEMS

We present techniques used in ADELE, a second-generation expert system (SGES), to support the knowledge acquisition activity in the diagnostic domain. The approach has been studied inside the framework of SGES; it is based on the reunions between knowledge acquisition and explanations. When new heuristic knowledge is acquired, its justifications are looked for in domain models to support the knowledge acquisition process. ADELE is a medical diagnostic reasoning system for electromyography.

INTEGRATED KNOWLEDGE ACQUISITION FROM TEXT, PREVIOUSLY SOLVED CASES, AND EXPERT MEMORIES

Within the model-based knowledge engineering framework, an integrated knowledge acquisition method was developed for a complex real-world domain with different traces of expertise. By having an expert constructively explain the previously solved cases with more general information from other traces of expertise ( text, expert memories) a model-centered knowledge base is constructed. The proposed method allows for an early knowledge verification where the relevance, sufficiency, redundancy, and consistency of knowledge are already assessed at an informal level The early knowledge verification efficiently prepares the consecutive knowledge formalization. Through a cognitively adequate model of expertise and the explanation-oriented knowledge elicitation procedures, user-friendly second-generation expert systems may be developed.

Domain modelling in second-generation expert systems

The attractions of incorporating explicit domain models within an expert system are summarised. The essential aspects of qualitative models suitable for reasoning about physical systems in particular are introduced. The form and use of such models is demonstrated through examples. Current limitations of these modelling techniques for reasoning about physical systems are identified. The paper concludes by stating some of the main issues relevant to domain modelling activities.

What is a deep expert system? An analysis of first-generation limitations and a review of second-generation architectures

First-generation expert systems have significant limitations, attributed to their not being sufficiently deep. This paper attempts to clarify the concept of deepness with respect to expert systems, by analysing the limitations of first-generation expert systems and reviewing proposed architectures for second-generation expert systems. Inherent in each architecture is a definition of deepness.

HOIST: a second-generation expert system based on qualitative physics

This article describes a causal expert system based on hypothetical reasoning and its application to the maintenance of the lower hoist of a Mark 45 turret gun. The system, Hoist, performs fault diagnosis without the use of a repair expert or shallow rules. Its knowledge is coded directly from a structural specification of the Mark 45 lower hoist. The technology reported here for assisting the less experienced diagnostician differs considerably from normal rule-based techniques: It reasons about machine failures from a functional model of the device. In a mechanism like the lower hoist, the functional model must reason about forces, fluid pressures, and mechanical linkages; that is, it must reason about qualitative physics. Hoist technology can be directly applied to any exactly specified device for the modeling and diagnosis of single or multiple faults. Hypothetical reasoning, the process embodied in Hoist, has general utility in qualitative physics and reason maintenance.

REX: Robotics expert systems building tool

REX, an expert systems development shell for robotics applications, is presented in the paper. The shell is intended for building expert systems in various domains of robotics, including design, planning, control, fault-detection, and navigation. REX's knowledge and data representation techniques include several standard techniques, like rules, frames, parameters, and variables, as well as certain techniques which are not commonly used by other expert systems building tools. These specific techniques include sensor data, status data, models and algorithms. The REX inference engine provides a means for reasoning with all of these knowledge and data types. The inference engine features both off-line and on-line modes of operation. REX has powerful interfaces for communicating both with the user and with other software modules in a complex application. Certain learning techniques are built into the shell. An example of applying REX for building an expert system in the domain of robot control is also presented. Finally, a discussion is provided on the relation between REX and second-generation expert systems.

What is a deep expert system? An analysis of the architectural requirements of second-generation expert systems

AbstractFirst-generation expert systems have significant limitations, often attributed to their not being sufficiently deep. However, a generally accepted answer to “What is a deep expert system?” is still to be given. To answer this question one needs to answer “Why do first-generation systems exhibit the limitations they do?” thus identifying what is missing from first-generation systems and therefore setting the design objectives for second-generation (i.e. deep) systems. Several second-generation architectures have been proposed; inherent in each of these architectures is a definition of deepness. Some of the proposed architectures have been designed with the objective of alleviating a subset, rather than the whole set, of the first-generation limitations. Such approaches are prone to local, non-robust solutions. In this paper we analyze the limitations (under the categories: human-computer interaction, problem-solving flexibility, and extensibility) of the first-generation expert systems thus setting design goals for second-generation systems. On the basis of this analysis proposed second-generation architectures are reviewed and compared. The paper concludes by presenting requirements for a generic second-generation architecture.

Expert Focus-expert system tools: the next generation

The authors describe what is wrong with conventional expert system development and consider whether these gaps can be filled by second-generation expert system development tools. They examine decision-tree approaches and knowledge shaping, which utilizes decision-tree power while eliminating problems commonly associated with their use. They conclude that knowledge shaping solves the problems encountered with present tools.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>