bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative Evaluation of Three Expert System Development Tools: Kee, Knowledge Craft, Art
Authors: Laurent Jean-Pierre , Ayel Jacqueline , Thome Franck , Ziebelin Danielle 
Published: July 2009

AbstractThe purpose of this paper is to evaluate and compare three of the most powerful expert system tools available: KEE from Intellicorp, Knowledge Craft from The Carnegie Group Inc., and ART from Inference Corporation.These three tools are industrial development environments which are fully supported and well beyond research prototypes. They were implemented on Lisp machines initially, but will soon be available on conventional computers. The three systems are very flexible and offer many ways of representing knowledge.The first part of the paper is a technical overview of each tool. The second part presents their common features. The third part discusses the advantages and drawbacks of each, according to types of application.This work has been partially sponsored by the Commission of the European communities within the Esprit Project 932 entitled ‘Knowledged-based Real-Time supervision in Computer Integrated Manufacturing’. We are grateful to Didier Gaget-Soufflot (Graphael, France) and Bernard Raust (CGE/TITN) for their participation in the evaluation of KEE and ART respectivily.
The control of reasoning under uncertainty: A discussion of some programs
Authors: Cohen Paul R. 
Published: July 2009

AbstractThis paper proposes that managing uncertainty is acontrolproblem, a task for the control component of AI systems that decides what to do next. This view emphasizes the process of planning and executing sequences of actions that simultaneously satisfy domain goals and minimize uncertainty. The paper reviews AI systems that manage uncertainty by control. It is not an exhaustive survey, but rather illustrates issues in managing uncertainty with selected AI programs.
Experiences with a knowledge engineering toolkit: an assessment in industrial robotics
Authors: Robinson V. , Hardy N.W. , Barnes D.P. , Price C.J. , Lee M.H. 
Published: July 2009

AbstractSome of the issues involved in purchasing and using one of the powerful Knowledge Engineering Toolkits are examined. A case study problem is described and several products are reviewed as potential environments for implementing a solution. Experiences of using one System, ART, are described and observations are made regarded the selection, evaluation and use of such packages.
Statistical applications of artificial intelligence and knowledge engineering
Authors: Gale William A. 
Published: July 2009

AbstractKnowledge engineering (KE) has now provided some effective techniques for formalization of knowledge about goals and actions. These techniques could open new areas of research to statisticians. Experimental systems designed to assist users of statistics have been constructed in experiment design, data analysis, technique application, and technique selection. Knowledge formalization has also been used in experimental programs to assist statisticians in doing data analysis and in building consultation systems. The best-explored application of KE techniques is building consultation systems. It is now a promising area for development. Analogies with successful artificial intelligence AI applications in other fields suggest other statistical applications worth exploring. Opening new areas to research and providing new tools to users would make considerable changes in the use and production of statistical techniques. However, applying currently available KE techniques will lead to more work for statisticians, not less.
Knowledge based interpretation of images: a biomedical perspective
Authors: Walker Nicholas , Fox John 
Published: July 2009

AbstractThe traditions of image processing and knowledge engineering have developed separately. Work on AI vision systems lies between the two traditions but only recently has attention been given to combining practical imaging systems with methods for exploiting knowledge in interpreting the contents of an image. Five general approaches to combining knowledge based expert systems with imaging technologies are discussed. Particular attention is paid to the requirement for techniques which transform a pixel array into a symbolic form suitable for interpretation, and current obstacles to a general solution. Interpretation of biomedical images is particularly problematic because of statistical, structural and temporal variation in morphology of objects and structures. Some ways in which knowledge of shape, structure, and object classifications may contribute to this interpretation are discussed. The survey focuses on biomedical images but many of the issues are of general relevance to work in image understanding.
A survey of French developments in knowledge-based systems
Authors: Rault Jean-Claude 
Published: July 2009

AbstractLike other industrialized countries, France is currently enjoying a vogue for artificial intelligence and, generally, for hardware and software components and structures which will be needed for the design and implementation of the computer applications of the 1990s.Since public announcement of MITI's Fifth Generation Project in October 1981, the French scientific and industrial communications have exhibited increasing enthusiasm for AI languages, expert systems, man-computer interaction, novel computer architectures, and knowledge-based computer systems as a whole. The choice of the Prolog language for the Japanese project has stimulated many French industrialists to be aware of the existence of a basic AI tool designed mainly in France.In spite of the present fashion, often maintained by the journalistic milieu, it would be inaccurate to say that the French fifth generation project goes back to the Japanese announcement. The MITI project has certainly been a catalyst of ministerial and industrial awareness, but the bulk of ongoing projects stem from earlier work most often funded by government agencies.In spite of the current thrust in AI and the centralizing habit in France, a “flagship” AI project cannot be identified. French Research and Development initiatives in artificial intelligence in general, and expert systems in particular, correspond more to a set of distinct projects. These frequently complement each other in technical scope and in their scientific and industrial objectives.
First generation expert systems: a review of knowledge acquisition methodologies
Authors: Neale Ian M. 
Published: July 2009

AbstractThis paper reviews a wide range of knowledge acquisition techniques in the context of attempts to achieve a systematic methodology. These have been poorly documented by expert system builders, who are often inclined to overvalue textbooks and the ways experts themselves claim they solve problems. No one method has a universal advantage; each has some value. Techniques should be selected to suit the domain, the task, the expert and the knowledge engineer. Knowledge acquisition involves creating a conceptual model of expert knowledge and reasoning, from analysis of data elicited by these techniques. A survey of the literature indicates increasing emphasis on tools for knowledge acquisition, used directly by experts. Several projects currently directed towards providing a proper epistemological foundation for knowledge acquisition are discussed and compared. None has yet produced a complete epistemologically sound methodology; however, recognition of the need to create a conceptual model at the knowledge level (rather than the symbol level) is an important advance. An extensive bibliography is appended.
Computational reflection
Authors: Maes Pattie 
Published: July 2009

AbstractComputational reflection is the activity performed by a computational System when reasoning about (and by that possibly affecting) itself. This paper presents an introduction to computational reflection (thereafter called reflection). A definition of reflection is presented, its utility for knowledge engineering is discussed and architectures of languages that support it are studied. Examples of such procedural, logic-based, rule-based and object-oriented languages are presented. The paper elaborates on the design of these languages and the reflective functionality that results, elucidating concepts such as procedural reflection, declarative reflection, theory relativity of reflection, etc. The paper concludes with an assessment of outstanding problems and future developments in the area.
Generic tasks as building blocks for knowledge-based systems: the diagnosis and routine design examples
Authors: Chandrasekaran B. 
Published: July 2009

AbstractThe level of abstraction of much of the work in knowledge-based systems (the rule, frame, logic level) is too low to provide a rich enough vocabulary for knowledge and control. I provide an overview of a framework called the Generic Task approach that proposes that knowledge systems should be built out of building blocks, each of which is appropriate for a basic type of problem solving. Each generic task uses forms of knowledge and control strategies that are characteristic to it, and are in general conceptually closer to domain knowledge. This facilitates knowledge acquisition and can produce a more perspicuous explanation of problem solving. The relationship of the constructs at the generic task level to the rule-frame level is analogous to that between high-level programming languages and assembly languages in computer science. I describe a set of generic tasks that have been found particularly useful in constructing diagnostic, design and planning systems. In particular, I describe two tools, CSRL and DSPL, that are useful for building classification-based diagnostic systems and skeletal planning systems respectively, and a high level toolbox that is under construction called the Generic Task toolbox.
An answer to commentators on the paper “Generic tasks as building blocks for knowledge-based systems: the diagnosis and routine design examples”
Authors: Chandrasekaran B. 
Published: July 2009

I thank the commentators for their time, and generally positive remarks on the promise of the task-specific approach, in particular the generic task (GT) proposal.Johnson and Zualkernan would like a methodology for mapping domain knowledge onto one or more generic tasks so as to solve problems efficiently. This stage of problem and domain analysis in which the kind of reasoning that goes on needs to be analysed in a vocabulary of generic tasks is very important, and in our laboratory we identify this as the epistemic analysis stage. For specific classes of problems we have developed guidelines on how to perform this mapping. For example, Bylander and Smith (Bylander and Smith, 1985) describe a set of criteria and guidelines for mapping medical knowledge into CSRL-like structures for diagnostic reasoning. Similarly Brown (1984) describes criteria for mapping design knowledge into DSPL-like structures.
Graphical interfaces for knowledge engineering: an overview of relevant literature
Authors: Jones Sara 
Published: July 2009

AbstractLiterature relevant to the design and development of graphical interfaces for knowledge-based systems is briefly reviewed and discussed. The efficiency of human-computer interaction depends to a large extent on the degree to which the human-machine interface can answer the user's cognitive needs and accurately support his or her natural cognitive processes and structures. Graphical interfaces can often be particularly suitable in this respect, especially in cases where the user's “natural idiom” is graphical. Illustrated examples are given of the way in which graphical interfaces have successfully been used in various fields with particular emphasis on their use in the field of knowledge-based systems. The paper ends with a brief discussion of possible future developments in the field of knowledge-based system interfaces and of the role that graphics might play in such developments.
Default non-monotonic logic
Authors: Mott Peter 
Published: July 2009

AbstractThis paper is a review of certain non-monotonic logics, which I call default non-monotonic logics. These are logics which exploit failure to prove. How each logic uses this basic idea is explained, and examples given. The emphasis is on leading ideas explained through examples: technical detail is avoided. Four non-monotonic logics are discussed: Reiter's default logic, McCarthy's circumscription, McDermott's modal non-monotonic logic, and Clarks's completed database. The first two are treated in some detail. The recent Hanks-McDermott criticism of non-monotonic logic is discussed, and some conclusions drawn about the prospects for non-monotonic logic. Recommendations for further reading are given.
Computer architectures for logic-oriented data/knowledge bases
Authors: Shin Donghoon , Berra P. Bruce 
Published: July 2009

AbstractKnowledge base management systems (KBMS) are designed to efficiently retrieve and manipulate large shared knowledge bases. A significant subclass of KBMS consisting of a combination of logic programming and database is often called a logic oriented knowledge base system (LOKBS). These systems must possess considerable processing and I/O capabilities so many approaches have been taken to the improvement of their performance. In this paper we review the current performance enhancing hardware approaches for LOKBS. We include parallelism, both in processing and I/O, algorithms, caching, and physical data organizations.
Interfacing a knowledge-based system to a large database
Authors: Gray P. M. D. , Archibald I. G. , Lunn K. 
Published: July 2009

AbstractThis paper describes the interfacing problem that arose in a Product Formulation expert system written in LISP that had to be interfaced to data in a relational database running on a separate mainframe computer. It surveys the different forms of coupling that are possible and emphasizes the advantages of tight navigational coupling over the more popular set-based coupling. It describes how Prolog was used to overcome the interfacing problems and to provide a customized front end to an end user, based on a navigational interface. It reviews the techniques of using Prolog and the likely obstacles, together with a look forward to databases using Frames or Objects.
Deductive database theories
Authors: Grant John , Minker Jack 
Published: July 2009

AbstractThis paper surveys a variety of deductive database theories. Such theories differ from one another in the set of axioms and metarules that they allow and use. The following theories are discussed: relational, Horn, and stratified in the text; protected, disjunctive, typed, extended Horn, and normal in the appendix. Connections with programming in terms of the declarative, fixpoint, and procedural semantics are explained. Negation is treated in several different ways: closed world, completed database, and negation as failure. For each theory examples are given and implementation issues are considered.
A Comparative Exploration of Concurrent Logic Languages
Authors: Ringwood G. A. 
Published: July 2009

AbstractThe execution model of Prolog, the first popular language based on Horn Clauses, was designed for efficient evaluation on von Neumann architectures. An alternative process model of execution, better suited for parallel evaluation and reactive programming, has given rise to a new class of languages based on Horn Clause logic, concurrent logic languages. There appears to be a profusion of languages which claim to fall into this class and it is difficult for an initiate to appreciate why each is the way it is. One notable member of this class, FGHC, forms the cornerstone of the Japanese 5th Generation Initiative. Fortunately, the seemingly exponential growth in these languages is only an illusion. A finite number of synchronization mechanisms arise from attempting (or sometimes not attempting) to control two principle synchronization difficulties: the premature binding problem and the binding conflict problem. Suitable combinations of these synchronization mechanisms reproduce the languages of this family. A background knowledge of Prolog is assumed and some familiarity with the difficulties encountered in concurrency would be advantageous.
Planning and decision theory
Authors: Haddawy Peter , Rendell Larry 
Published: July 2009

Research on planning in AI can be separated into the two major areas: plan generation and plan representation. Most AI planners to date have been based on the STRIPS planning representation. This representation has a number of limitations. Much recent work in plan representation has addressed these limitations. It was shown that Decision Theory can be used to remove a number of the limitations. Furthermore, the decision theoretic framework provides a precise definition of rational behaviour. There remain open questions within decision theory regarding belief revision and causality. It should be noted that these problems are not artifacts of the representation. Rather they arise because the rich representation allows their formulation. Some work integrating AI and decision theoretic approaches to planning has been done but this remains a largely untouched research area.We see two main avenues for fruitful research. First, the straightforward decision theoretic formulation of planning is computationally impractical. Techniques need to be developed to do efficient decision theoretic planning. Work in AI plan generation has exploited information contained the structure of qualitative representations to guide efficient plan construction. These techniques should be applied to decision theoretic representations as well. Second, AI has developed many representations that allow useful structuring of knowledge about the world. Decision Theory has concentrated on representing beliefs and desires. Integration of AI and decision theoretic representations would yield powerful representation languages. Some of the benefits of such work can already be seen in the research combining temporal and decision theoretic representations.
Medical AI systems as appropriate technology for developing countries
Authors: King Kathleen , Beck Howard 
Published: July 2009

AbstractExpert systems technology has been around for a long time, becoming increasingly easy to use, inexpensive and reliable in recent years. It would seem to provide an ideal vehicle for the dissemination of expertise in developing countries, particularly in the field of medicine, which was the focus of much early work in diagnostic systems. Despite the apparent match of a real problem and a credible solution, however, remarkably few AI systems for medicine in developing countries have been researched, designed and implemented. This paper addresses why this might be the case, reviews some of the extant systems and explores some of the design issues. Particular emphasis is placed on the question of “Appropriate Technology”. Various criteria for Appropriate Technology are explored, and an optimal set used to guide principles of design. It is argued that medical AI systems can satisfy these criteria, provided that sufficient care is taken in their design for the country of application.
Lighthill 17 years on
Authors: Lam Martin 
Published: July 2009

AbstractIt is a common Establishment practice in the UK to ask an expert, recognized for his/her sound views, to give a view on controversial matters of public interest. There are recent examples in such varied fields as accounting standards, export credits, and microgravity. Administrations—for all the current fashion for “built-in evaluation”—have no time or inclination to take a look backwards at these autographs. In this paper the author, who was a civil servant, and has worked for ten years as a consultant, including two years in the European Commission, considers, with the help of hindsight, the Lighthill Report of 1972 on AI.This review implies making judgements about the progress of AI which cannot help being contentious. First, there will be disagreements about judgements, or even assertions—the author cannot claim authority over the whole field, unless as a voyeur; there may be important developments he has failed to take account of. Second, there will be arguments that Lighthill was to an extent self-fulfilling—that is, that there would have been more advance if some aspects of AI had not been checked at his advice. Third, it will be said that it takes time to work up a new computational concept—or any other new thing.However, whatever the reservations, the main conclusion put forward is that there was an element of over-optimism about AI in general which Lighthill had reason to castigate. In directing his comments to the utility of AI in specific fields Lighthill got some things wrong, but, on balance—which is not easy to strike—probably more things right than wrong.Extrapolating to the present day the writer questions whether, in the UK after Alvey, and not least in Japan, resources are not being expended on AI objectives which are intellectually stimulating but remain too far from the application's sharp end; and therefore whether there is still not too much “jam tomorrow” about AI. Whether or not this questioning is judged to be wrongheaded, perhaps it will prove useful to have stirred discussion on the subject.
Constraint logic programming
Authors: Van Hentenryck Pascal 
Published: July 2009

AbstractConstraint logic programming (CLP) is a generalization of logic programming (LP) where unification, the basic operation of LP languages, is replaced by constraint handling in a constraint system. The resulting languages combine the advantages of LP (declarative semantics, nondeterminism, relational form) with the efficiency of constraint-solving algorithms. For some classes of combinatorial search problems, they shorten the development time significantly while preserving most of the efficiency of imperative languages. This paper surveys this new class of programming languages from their underlying theory, to their constraint systems, and to their applications to combinatorial problems.
Functional programming languages for AI problem solving
Authors: Bradley Eleanor 
Published: July 2009

AbstractMany problem domains exhibit inherent parallelism, and parallel systems which capture and exploit this can be used to look for efficient solutions to AI problems. Functional programming languages are expected to be efficiently realisable on fifth generation hardware. A rational reconstruction of AI programming paradigms is used to investigate the programmability and performance of functional languages in this particular area.Three languages—Standard ML, Hope+ and Miranda—are used in the rational reconstruction, each language being used to implement three applications. Results indicate that functional programming languages have become much more useable in recent years, and have the potential to become useful tools in AI problem solving. A brief annotated bibliography of texts which covers the introduction to, theory and implementation issues of, functional programming languages, is included.
Principles of induction and approaches to attribute based induction
Authors: Kalkanis G. , Conroy G. V. 
Published: July 2009

AbstractThis paper presents a survey of machine induction, studied mainly from the field of artificial intelligence, but also from the fields of pattern recognition and cognitive psychology. The paper consists of two parts: Part I discusses the basic principles and features of the machine induction process; Part II uses these principles and features to review and criticize the major supervised attribute-based induction methods. Attribute-based induction has been chosen because it is the most commonly used inductive approach in the development of expert systems and pattern recognition models.