bluuebunny/crossref_metadata_2025_split huggingface dataset:
Engineering experts critics for cooperative systems
Authors: Silverman Barry G. , Wenig R. Gregory 
Published: July 2009

AbstractKnowledge collection systems often assume they are cooperating with an unbiased expert. They have few functions for checking and fixing the realism of the expertise transferred to the knowledge base, plan, document or other product of the interaction. The same problem arises when human knowledge engineers interview experts. The knowledge engineer may suffer from the same biases as the domain expert. Such biases remain in the knowledge base and cause difficulties for years to come.To prevent such difficulties, this paper introduces the reader to “critic engineering”, a methodology that is useful when it is necessary to doubt, trap and repair expert judgment during a knowledge collection process. With the use of this method, the human expert and knowledge-based critic form a cooperative system. Neither agent alone can complete the task as well as the two together.The methodology suggested here offers a number of extensions to traditional knowledge engineering techniques. Traditional knowledge engineering often answers the questions delineated in generic task (GT) theory, yet GT theory fails to provide four additional sets of questions that one must answer to engineer a knowledge base, plan, design or diagnosis when the expert is prone to error. This extended methodology is called “critic engineering”.
Knowledge-based expert systems — The State of the Art in the US
Authors: Hayes-Roth F. 
Published: July 2009

SummaryThis paper aims to describe the current state of knowledge systems technology and its commercialisation in the US. First, knowledge systems are defined and placed in a historical context. The introduction is concluded with a preview of major ideas. The paper will assess the technological state of the art and will survey the current state of commercialisation. Finally, some anticipated future trends will be discussed.
Criteria for Choosing Representation Languages and Control Regimes for Expert Systems
Authors: Reichgelt Han , van Harmelen Frank 
Published: July 2009

AbstractShells and high-level programming language environments suffer from a number of shortcomings as knowledge engineering tools. We conclude that a variety of knowledge representation formalisms and a variety of controls regimes are needed. In addition guidelines should be provided about when to choose which knowledge representation formalism and which control regime. The guidelines should be based on properties of the task and the domain of the expert system. In order to arrive at these guidelines we first critically review some of the classifications of expert systems in the literature. We then give our own list of criteria. We test this list applying our criteria to a number of existing expert systems. As a caveat, we have not yet made a systematic attempt at correlating the criteria and different knowledge representations formalisms and control regimes, although we make some preliminary remarks throughout the paper.
Cognitive emulation in expert system design
Authors: Slatter Philip E. 
Published: July 2009

SummaryCognitive emulation is an expert System design strategy which attempts to model System performance on human (expert) thinking. Arguments for and against cognitive emulation are reviewed. A major conclusion is that a significant degree of cognitive emulation is an inherent feature of design, but that an unselective application of the strategy is both unrealistic and undesirable. Pragmatic considerations which limit or facilitate the viability of a cognitive emulation approach are discussed. Particular attention is given to the conflict between cognitive emulation and established knowledge engineering objectives, detailed over 12 typical expert System features. The paper suggests circum-stances in which a strategy of cognitive emulation is useful.
Knowledge transfer: a key to successful application of knowledge-based systems
Authors: Niwa Kiyoshi 
Published: July 2009

AbstractFor successful application of knowledge-based systems, “knowledge transfer” (KT) should be receiving more attention. Knowledge transfer is defined as a flow of knowledge from knowledge suppliers through knowledge-based systems to users. Insufficient state-of-the-application of knowledge-based systems in Japan and its near future directions are analysed in terms of KT. Some example tasks for improving KT are reported. These tasks were experienced by the author in the course of 10 years of research and development on knowledge-based systems for project management.
Commentary on the commercialization of knowledge engineering: enterprise and product development
Authors: Wess Bernard P. 
Published: July 2009

AbstractThis article outlines the basic corporate engineering process that leads to the commercialization of a knowledge engineering concept, or software process. The development of a commercial product and enterprise from a technology-driven research effort or idea is quite similar to the process of continual refinement and hard work demanded by the scientific method, so the fundamentals of corporate and product development should not appear foreign to academics or those involved in corporate research. Knowledge engineering and related disciplines are on the verge of significant commercial product development efforts. The process of commercialization of ideas, particularly those derived from academic and research environments may not be intuitively obvious to the KE professional. The author outlines the general process of commercializing KE products and describes the three primary functional activities required to undertake a successful technology-driven commercial enterprise—market and customer definition, the development of the product prototype and corporate infrastructure, and capitalization.
A strategy for near-term success using knowledge-based systems
Authors: Laufmann S. C. 
Published: July 2009

AbstractKnowledge-based System (KBS) technologies have been applied to a variety of knowledge-related tasks with varying degrees of success. Differentiating among classes of knowledge-related tasks, based on the amounts of problem-solving knowledge and case-specific data involved, can provide valuable insight into why this occurs. Based on this comparison, four classes of problems are described. One class, of data-intensive tasks, includes problem types that are difficult or impossible for humans to perform, yet may be solved in a cost-effective manner using currently accessible KBS technology. The characteristic features of problems in this class are given, together with an example of a successfully fielded knowledge-based system that solves a problem from this class.
Learning to use the S.1 knowledge engineering tool
Authors: Ward R.D. , Sleeman D. 
Published: July 2009

AbstractIt is often claimed that it is easy to write expert systems. This claim was examined by monitoring experienced programmers learning to use the S.I knowledge engineering tool. Their achievements and difficulties were examined using a framework that has emerged from previous research into novices learning to use standard programming languages. Even though the experienced programmers all had several years' experience of programming in more than one standard language, there were similarities between their difficulties in learning to use S.I and the difficulties of complete novices learning to program in standard languages.The experienced programmers were however able to overcome their initial difficulties fairly quickly, but it is argued that complete novices would not find it so easy to do so. Also the experienced programmers did take time to develop a repertoire of schemeta for representing different kinds of factual, judgemental and procedural knowledge. It was concluded that in S.1, as with other programming languages and softwares tools, it is easy to learn how to do simple things, but difficult, even for experienced programmers to learn how to do more complex things.No criticism of S.1 is implied. S.1 was found to be a suitable vehicle for introducing non-trivial knowledge engineering concepts, and we believe that similar difficulties would occur in learning to use other knowledge engineering tools.
Experience using S.1: an expert system for newspaper printing press configuration
Authors: Lan M.S. , Panos R.M. , Balban M.S. 
Published: July 2009

AbstractThis paper describes some of our experience gathered during the development of an expert system, the press lineup advisor, in which we used the commercially available expert system development tool, S.1.™ Discussion includes: (1) how we used S. l to develop a system which solves a configuration problem; (2) difficulties we encountered when applying S.1 to this specific reasoning problem; (3) limitations of S.1 from both problem-solving and operational points of view and (4) issues remaining to be solved with respect to generalization of the system.
Expert systems and hypertext
Authors: Rada Roy , Barlow Judith 
Published: July 2009

AbstractExpert systems and hypertext are technologies which are coming together. This paper reviews the strengths and weaknesses of expert systems and hypertext. Then the complementarity of the two is both argued and illustrated. Translating expertise from documents into expert systems is difficult, but hypertext systems exploit the information in documents. Expert systems don't explain their decisions as well as some people would like, but hypertext is basically designed to provide explanations. On the other hand, people have difficulty deciding what links to follow in hypertext, but an expert system is designed to help people make decisions. When a hypertext reader is confused as to what step to follow next, an expert system might review the path taken thus far and suggest the appropriate next step. An annotated bibliography of the principal literature is provided.
Logics for reasoning about knowledge and belief
Authors: Reichgelt Han 
Published: July 2009

AbstractAI researchers are becoming increasingly aware of the importance of reasoning about knowledge and belief. This paper reviews epistemic logic, a logic designed specifically for this type of reasoning. I introduce epistemic logic, and discuss some of the philosophical problems associated with it. I then compare two different styles of implementing theorem provers for epistemic logic. I also briefly discuss autoepistemic logic, a form of epistemic logic intended to model an agent's introspective reasoning, i.e. an agent's reasoning about its own beliefs. Finally, I discuss some of the proposals in the AI literature that are aimed at avoiding some of the philosophical problems that dog both epistemic and autoepistemic logic. This paper is not a full introduction to the field. Rather, it is intended to give the reader some flavour of the problems that research in this area faces, as well as some of the proposals for solving these problems.
Life cycles in software and knowledge engineering: a comparative review
Authors: Wilson Michael , Duce David , Simpson Dan 
Published: July 2009

AbstractProgress in software engineering has led to system development following models of the system life cycle. These models incorporate the use of prototyping and formal methods of program verification. They are becoming supported by integrated project support environments and permit the planning and monitoring of software development projects.In contrast, knowledge based systems (KBS) are developed using informal views of the system life cycle. Tools have been developed to support some stages of the life cycle in an undisciplined manner. The commercial use of KBS needs development projects to be planned and monitored. This requires methods and tools based on systematic life cycle models to be established for KBS.This paper reviews the current state of life cycle approaches to software engineering and KBS development projects in order to provide a direction for the development of methodical KBS life cycle models.
What is a deep expert system? An analysis of the architectural requirements of second-generation expert systems
Authors: Keravnou E. T. , Washbrook J. 
Published: July 2009

AbstractFirst-generation expert systems have significant limitations, often attributed to their not being sufficientlydeep. 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.
Behavioural decision theory and it's implication for knowledge engineering
Authors: Lehner Paul E. , Adelman Leonard 
Published: July 2009

AbstractThis paper explores the implications of research results in behavioural decision theory on knowledge engineering. Behavioural decision theory, with its performance (versus process) orientation, can tell us a great deal about the validity of human expert knowledge, and when it should be modelled. A brief history of behavioural decision theory is provided. Implications for knowledge elicitation and representation are discussed. An approach to knowledge engineering is proposed that takes into account these implications.
Applications of artificial intelligence and expert systems in power engineering
Authors: Wong Kit Po 
Published: July 2009

AbstractThe theme of this paper is to review work carried out in the area of applying expert systems and artificial intelligence in electrical power engineering. It surveys expert systems and artificial-intelligence-based algorithms developed for solving decision problems in power network and generator fault diagnosis, reactive power and voltage control, restoration of power supply, determination of load circuits for shedding in under-frequency load shedding schemes, generator scheduling, allocation of loads and circuits, load-flow planning, generation expansion planning and distribution expansion planning.
Intelligent Systems in aerospace
Authors: Ali Moonis 
Published: July 2009

AbstractAn overview of research in the areas of aerospace applications of artificial intelligence, expert Systems, neural networks and robotics is presented. Challenges associated with aerospace projects require increasingly complex aerospace Systems which in turn demand automation and fault tolerance. We have addressed these issues and provided a survey of the research on intelligent Systems that has been carried out in an attempt to meet these challenges. The application areas we have overviewed include fault monitoring and diagnosis, generation and management of power in space, efficient and effective command and control, operations and maintenance of space stations, planning and scheduling, automation, and cockpit management.
Applications and prospects for AI in mechanical engineering design
Authors: Carter Ian M. 
Published: July 2009

AbstractMechanical engineering design is a broad subject area covering many topics and bas influences upon many other engineering disciplines and activities. Computer support for mechanical engineering design activity has been in draughting Systems and analysis packages, but there has been little in conceptual design assistance. This paper presents a number of areas of work in which AI techniques and developments are being used, sometimes in conjunction with traditional methods, to improve the support of design. The approaches to design and design Systems are covered, along with some techniques that are used. Specifie design Systems illustrate progress, and integration issues and simultaneous engineering Systems indicate the way research is moving. Finally, discussion of the trends and future topics indicates where and how effort may be applied in the future.
Natural language interfaces to databases
Authors: Copestake Ann , Jones Karen Sparck 
Published: July 2009

AbstractThis paper reviews the current state of the art in natural language access to databases. This has been a long-standing area of work in natural language processing. But though some commercial systems are now available, providing front ends has proved much harder than was expected, and the necessary limitations on front ends have to be recognized. The paper discusses the issues, both general to language and task-specific, involved in front end design, and the way these have been addressed, concentrating on the work of the last decade. The focus is on the central process of translating a natural language question into a database query, but other supporting functions are also covered. The points are illustrated by the use of a single example application. The paper concludes with an evaluation of the current state, indicating that future progress will depend on the one hand on general advances in natural language processing, and on the other on expanding the capabilities of traditional databases.
Explaining reasoning: an overview of explanation in knowledge-based systems
Authors: Southwick Richard W. 
Published: July 2009

AbstractThere seems to be general agreement amongst those involved in KBS research that in order to be useful, a system must be able to explain its reasoning to a user. This paper reviews the development of explanation facilities in knowledge-based systems. It differentiates between explanation as a problem-solving process, and that which explains a reasoning process. This review concentrates on the latter, identifying and giving examples of three categories of reasoning explanation.We then look at user requirements for explanation. What makes an explanation useful depends on the expectations of a user, which in turn depends on such issues as user background and system context. Several techniques are examined that have been applied to the problem of producing explanations that are appropriately structured and conveyed.Finally, we discuss some of the work that has been done in describing theories of human discourse and explanation, and some issues that will become increasingly important for future explanation systems.An extensive annotated bibliography is provided.
A review and synthesis of user modelling in intelligent systems
Authors: Kok André J. 
Published: July 2009

AbstractThis paper gives a state-of-the-art overview of the rapidly expanding field of user modelling in artificially intelligent systems. After showing how a user modelling component can improve parts of the processing of, and the interaction with, a large number of systems, the current situation in this field is sketched by means of a number of short descriptions of systems employing user modelling techniques. Next, a synthesis of this review is made by discussing the aspects on which the existing methods differ. It turns out that these aspects can best be approached from two points of view: the technical level and a more abstract, functional level. This dichotomy results in eight dimensions on which to compare the modelling methods. The synthesis is then completed by describing the existing modelling techniques, and classifying the reviewed systems. In the final section, current trends in the field are outlined and prerequisites for acceptance of user modelling on a larger scale are discussed.
Towards knowledge-based tutors: a survey and appraisal of Intelligent Tutoring Systems
Authors: Sokolnicki Tomas 
Published: July 2009

AbstractIntelligent tutoring systems can be seen as a next step for computer-based training systems, but also as an important by-product of knowledge-based expert systems. This paper surveys the development and progress in the area, with a special emphasis on the potential for an emerging engineering discipline as opposed to a mere crafting of systems. Major components in intelligent tutoring systems as realized so far are discussed, and key issues for successful future development identified. Knowledge representation, student modelling, planning, natural language issues, explanations and learning are discussed in more depth as being the cornerstones of both tutoring systems and artificial intelligence. Examples from specific implementations are used to illustrate key points.
In the concluding discussion we present our attempt at dealing with some of the problems facing the area. In the project Knowledge-Linker, we aim at extending the functionality of a knowledge-based system with tutoring capabilities, and suggest one way of explicitly dealing with teaching strategies.
A review of learning
Authors: Kocabas S. 
Published: July 2009

AbstractLearning is one of the important research fields in artificial intelligence. This paper begins with an outline of the definitions of learning and intelligence, followed by a discussion of the aims of machine learning as an emerging science, and an historical outline of machine learning. The paper then examines the elements and various classifications of learning, and then introduces a new classification of learning based on the levels of representation and learning as knowledge-, symboland device-level learning. Similarity- and explanation-based generalization and conceptual clustering are described as knowledge level learning methods. Learning in classifiers, genetic algorithms and classifier systems are described as symbol level learning, and neural networks are described as device level systems. In accordance with this classification, methods of learning are described in terms of inputs, learning algorithms or devices, and outputs. Then there follows a discussion on the relationships between knowledge representation and learning, and a discussion on the limits of learning in knowledge systems. The paper concludes with a summary of the results drawn from this review.
Computational models of scientific discovery
Authors: Kocabas Sakir 
Published: July 2009

AbstractComputational modelling of scientific discovery is emerging as an important research field in artificial intelligence. Various computational systems modelling different aspects of scientific research and discovery have been developed. This paper looks at some of these models in order to examine how knowledge is organized in such systems, what forms of representation they have, how their methods of learning and representation are integrated, and the effects of representation on learning. The paper also describes the achievements and shortcomings of these systems, and discusses the obstacles in developing more comprehensive models.
Conflict resolution strategies in expert scheduling systems: a survey and case study
Authors: Liebowitz Jay , Lightfoot Patricia , Dent Carolyn 
Published: July 2009

AbstractIn the coming years, scheduling will be an increasingly important activity in the manufacturing and aerospace industries, especially at the US National Aeronautics and Space Administration (NASA). Expert systems have successfully been used to aid in the scheduling function. This paper surveys some of the conflict resolution approaches used in building expert scheduling systems, and then examines the feasibility of using an expert systems approach for scheduling activities and resolving conflicts for experimenters to use NASA Goddard-supported satellites. Recommendations for further reading in expert scheduling systems are given.