bluuebunny/crossref_metadata_2025_split huggingface dataset:
Metalogic machines: a retrospective rationale for the Japanese Fifth Generation
Authors: Ringwood G. A. 
Published: July 2009

AbstractThe oft quoted inadequacy of von Neumann architectures for AI applications has regularly been used to justify the design of special purpose parallel machines. In particular, the von Neumann computational model has been criticized as being unsuitable for parallelism because of the memory access bottleneck. For the design of a new machine both top-down and bottom-up methodologies have drawbacks. The middle-out strategy, working both up and down from an intrinsically concurrent high-level programming language as a means of both representing and processing knowledge provides an attractive way of providing a symbiosis between software and hardware. The longest established and most well-founded symbolic method for the representation and manipulation of knowledge is logic. A notable result of the last decade, work on mechanical theorem proving was that a subset of predicate logic, Horn Clauses, can form the foundation of a computational model. The execution model of Prolog, the first popular language based on Horn Clauses, was designed for efficient evaluation on von Neumann architectures. An alternative computational model, more suitable for expressing reactive systems but retaining Prolog's affinity for metaprogramming, has given rise to a new class of languages, concurrent logic languages. One among many of these languages, FGHC (flat Guarded Horn Clauses), was developed by the Japanese as the kernel of their Fifth Generation initiative. A background familiarity with Prolog would be helpful in understanding this article.
Parallelism in knowledge-based machines
Authors: Refenes Apostolos N. 
Published: July 2009

AbstractThe application area of knowledge-based expert systems is currently providing the main stimulus for developing powerful, parallel computer architectures. Languages for programming knowledge-based applications divide into four broad classes: Functional languages (e.g. LISP), Logic languages (e.g. PROLOG), Rule-Based languages (e.g. OPS5), and, what we refer to as self-organizing networks (e.g. BOLTZMANN machines).Despite their many differences, a common problem for all language classes and their supporting machine architectures is parallelism: how to de-compose a single computation into a number of parallel tasks that can be distributed across an ensemble of processors. The aim of this paper is to review the four types of language for programming knowledge-based expert systems, and their supporting parallel machine architectures. In doing so we analyze the concepts and relationships that exist between the programming languages and their parallel machine architectures in terms of their strengths and limitations for exploiting parallelization.
Applications of nonmonotonic logic to diagnosis
Authors: Jackson Peter 
Published: July 2009

AbstractThis paper attempts to assess the practical utility of nonmonotonic logic in diagnostic problem solving. We begin with a brief review of the main assumptions which motivate work in this area, and discuss two logic-based approaches which involve nonmonotonic arguments. Then we consider two recent proposals for the application of default logic to diagnosis, as well as a proposal based on counterfactual logic. In conclusion, we briefly compare these methods with other diagnostic reasoning paradigms found in the Artificial Intelligence literature.
A review of temporal logics
Authors: Long Derek 
Published: July 2009

AbstractA series of temporal reasoning tasks are identified which motivate the consideration and application of temporal logics in artificial intelligence. There follows a discussion of the broad issues involved in modelling time and constructing a temporal logic. The paper then presents a detailed review of the major approaches to temporal logics: first-order logic approaches, modal temporal logics and reified temporal logics. The review considers the most significant exemplars within the various approaches, including logics due to Russell, Hayes and McCarthy, Prior, McDermott, Allen, Kowalski and Sergot. The logics are compared and contrasted, particularly in their treatments of change and action, the roles they seek to fulfil and the underlying models of time on which they rest. The paper concludes with a brief consideration of the problem of granularity—a problem of considerable significance in temporal reasoning, which has yet to be satisfactorily treated in a temporal logic.
Explanation and dialogue
Authors: Gilbert Nigel 
Published: July 2009

AbstractRecent approaches to providing advisory knowledge-based systems with explanation capabilities are reviewed. The importance of explaining a system's behaviour and conclusions was recognized early in the development of expert systems. Initial approaches were based on the presentation of an edited proof trace to the user, but while helpful for debugging knowledge bases, these explanations are of limited value to most users. Current work aims to expand the kinds of explanation which can be offered and to embed explanations into a dialogue so that the topic of the explanation can be negotiated between the user and the system. This raises issues of mutual knowledge and dialogue control which are discussed in the review.
Efficient knowledge representation systems
Authors: Giuse Dario 
Published: July 2009

AbstractFrame systems occupy an important place among formalisms for computer-based knowledge representation. A common concern about frame systems, however, is that they are not efficient enough. We argue that this is not necessarily true of all possible systems, and that the trade-off between generality and efficiency has not been fully explored. While many systems provide generality at the expense of performance, systems closer to the low end of the spectrum have not been investigated nearly as much. Those systems are well suited for applications that need flexible knowledge representation but cannot afford the high performance price.We describe in detail KR, a very efficient frame system that provides mechanisms for knowledge
representation including user-defined inheritance and relations, object-oriented programming, and
constraint maintenance. The system is simple and compact and does not include some of the more complex functionality, but it is highly optimized and offers excellent performance for a variety of applications.
AI applications in process design, operation and safety
Authors: Hutton D. , Ponton J. W. , Waters A. 
Published: July 2009

AbstractThis review provides an overview of the present state of the art in applying AI techniques to the work of Chemical and Process Engineers. Such work is described, where necessary, in order to make this paper understandable to an audience outside the process engineering community.
The first section discusses the nature of chemical engineering design and draws the important distinction between process design and plant design. The complete design cycle is covered a stage at a time and AI applications and issues identified. The section finishes with a discussion of integrated or concurrent design, which is the ultimate aim of much current research.
The second section discusses AI applications in process operation. This covers process monitoring, alarm processing and fault diagnosis.
The third section discusses, in some depth, design stage loss prevention, that is the design of plants to be safe and reliable. Safety is an integral part of design, but the inclusion of a separate section is appropriate as the qualitative and judgemental nature of many safety related problems has encouraged the use of AI techniques. Applications of AI planning to the problem of automatic operating procedure synthesis and applications of qualitative simulation to chemical engineering problems are also discussed in this section.
AI in control Systems engineering
Authors: Linkens D. A. 
Published: July 2009

AbstractDecision-making is an integral part of any consideration of the discipline of control Systems. This implies feedback of knowledge in some format, whether it is mediated via human or machineoriented methods. The application of AI methodologies to control Systems engineering is described in this review under two broad categories of design and implementation. The design of control strategies is itself divided into phases of modelling and simulation, identification, and algorithm selection and tuning. The aspects of implementation which are covered include real-time considerations such as knowledge-based control and fuzzy logic, multi-sensor data fusion, fault detection and Human–Computer Interaction (HCI).
Case research on knowledge acquisition: observations and lessons
Authors: Chan Christine , Benbasat Izak 
Published: July 2009

AbstractExpert systems are being built despite the widely acknowledged problem of acquiring knowledge from experts. This study attempts to understand how knowledge acquisition is conducted in practice by investigating three expert system development projects. A CASE research methodology is adopted, and data is collected through unobtrusive observation, from taped protocols of knowledge acquisition sessions, retrospective interviews with the participants involved, and deliverables produced. The variables examined include the problem domain, the domain expert, the knowledge engineer, the knowledge acquisition process, the expert system construction process, potential users, organizational setting, and the expert system itself. The knowledge acquisition processes for three expert systems in the domains of law of negligence, telephone line fault diagnosis, and wastewater treatment have been examined. By juxtaposing the observations drawn with findings from the relevant literature, the study makes prescriptive suggestions on considerations and techniques for future acquisition efforts, and provides data for hypothesis generation in further research.
Qualitative frameworks for decision support: lessons from medicine
Authors: Fox John , Krause Paul 
Published: July 2009

AbstractSome weaknesses of current decision support technologies are discussed. Numerical methods have strong theoretical foundations but are representationally weak, and only deal with a small part of the decision process. Knowledge-based systems offer greater flexibility, but have not been accompanied by a clear decision theory. Theoretical development ofsymbolic decision proceduresis advocated, an approach to the design of decision support systems based on first-order logic is presented, and work on this approach is reviewed. A central proposal is an extended form of inference calledargumentation; reasoning qualitatively for and against decision options from generalized domain theories. Argumentation captures a natural and familiar form of reasoning, and contributes to the robustness, flexibility and intelligibility of problem solving, while having a clear theoretical basis. Argumentation was developed initially for medical applications though it may have much wider applicability.
Overview of current practice and research initiatives for the verification and validation of KBS
Authors: Lydiard T. J. 
Published: July 2009

AbstractBased on a survey of recent literature, this report aims to highlight the issues associated with the verification and validation of knowledge based systems. The confusion arising from the lack of clear terminology is considered, along with some of the characteristics of knowledge based systems that cause particular difficulties for verification and validation. The various approaches that can be adopted to address these difficulties are discussed, followed by a survey of recent research initiatives.The author concludes that many of the difficulties associated with the verification and validation of knowledge based systems are a feature of the complexity of the system being built and the manner of its development rather than of the specific technology chosen to implement it.
Model-based reasoning in financial domains
Authors: Hamscher Walter 
Published: July 2009

AbstractFinance is a challenging yet appropriate domain for model-based reasoning, an area of research otherwise grounded in classical physics. Among the many features that suggest a model-based approach are that firms have formal internal structures, business entities have idealizable behaviours, and there is a history of formal analysis of business problems. This article discusses the motivations and foundations of the model-based approach, and surveys several existing artificial intelligence programs that exploit its advantages. The survey shows that there are ample opportunities for useful systems and significant research in this area. However, accomplishing either of these goals depends crucially upon moving beyond qualitative models based only on accounting information, which tend not to capture the actual complexities of the domain.
Research developments in multiple inheritance with exceptions
Authors: Eklund Peter W. 
Published: July 2009

AbstractThe inheritance problem can be simply stated: for any instantiation of an inheritance network, say a specific hierarchy Γ, find a conclusion set for Γ. In other words, find out what is logically entailed by Γ. This can be done in two ways: either by defining a deductive or proof theoretic definition to determine what paths are entailed by a network; or by translating the individual links in the network to a more general nonmonotonic logic and using its model and proof theory to generate entailments that correspond to what one would expect from “viewing” the inheritance hierarchy. Two approaches to a solution to the inheritance problem structure this paper. The first is widely known as the “path-based” or “proof theoretic”, and the second, the “Model-based” or “model theoretic”. The two approaches result in both a different interpretation of default links as well as a variation in the entailment strategy for a solution to teh inheritance problem. In either case, the entailments produced need some intuitive interpretation, which can be either credulous or skeptical. The semantics of both skeptical and credulous inheritance reasoners are examined.
A review of functionality modelling in design
Authors: Winsor Julian , MacCallum Kenneth 
Published: July 2009

AbstractRecently there has been an increase in the number of computer aided design systems developed explicitly representing knowledge about the functionality of engineering designs. Reviewing these systems provides an understanding of the methods workers use to encapsulate knowledge of functionality within their systems. A number of issues are addressed to reveal the nature of their approaches. The developers' perception of functionality is discussed to identify variations in understanding of function and to establish the existence of any consensus. Methods of representing this knowledge are examined, thereby identifying representation types or combinations used and the advantages to be gained from any single representation. Illustrations of the manipulation of function shows how this type of knowledge can be used to support reasoning during early stage design. A survey of relationships with other design characteristics as a testimony to the manipulation of functionality is used to impact other aspects of a design. Through knowledge of relationships some models of the design process are posited by workers. A study of these bears evidence of the role of function in design and the stages at which its use is significant.
Non-standard theories of uncertainty in knowledge representation and reasoning
Authors: Duboid Didier , Prade Henri 
Published: July 2009

AbstractThis paper provides a survey of the state of the art in plausible reasoning, that is exception tolerant reasoning under incomplete information. Three requirements are necessary for a formalism in order to cope with this problem: (i) making a clear distinction between factual information and generic knowledge; (ii) having a correct representation of partial ignorance; (iii) providing a nonmonotonic inference mechanism. Classical logic fails on requirements (i) and (iii), whilst the Bayesian approach does not fulfil (ii) in an unbiased way. In this perspective, various uncertainty modelling frameworks are reviewed: MYCIN-like fully compositional calculi, belief functions, upper and lower probability systems, and possibility theory. Possibility theory enablesclassicallogic to be extended to layered sets of formulae, where layers express certainty levels. Finally, it is explained how generic knowledge can be expressed by constraints on possibility measures, and how possibilistic inferences can encode nonmonotonic reasoning in agreement with the Lehmann et al. postulates.
Bias in human judgement under uncertainty?
Authors: Ayton Peter , Pascoe Eva 
Published: July 2009

AbstractThe claim is frequently made that human judgement and reasoning are vulnerable to cognitive biases. Such biases are assumed to be inherent in that they are attributed to the nature of the mental processes that produce judgement. In this paper, we review the psychological evidence for this claim in the context of the debate concerning human judgemental competence under uncertainty. We consider recent counter-arguments which suggest that the evidence for cognitive biases may be dependent on observations of performance on inappropriate tasks and by comparisons with inappropriate normative standards. We also consider the practical implications for the design of decision support systems.
CBT II—Case-based computer-aided instruction: survey of principles, applications and issues
Authors: Khan Tariq , Yip Yau Jim 
Published: July 2009

AbstractIn this paper we consider the role case-based reasoning has had in development of computer-aided instruction systems. We survey several case-based teaching systems, each of which is representative of a basic pedagogical principle that motivated its development. Firstly, 15 pedagogical principles are presented that were identified from the analysis of case-based teaching systems. We present some background to the principles, and indicate which systems they are incorporated in. Next, the teaching systems themselves are described, with emphasis on how case-based reasoning has been applied. Finally, we discuss some problems to be addressed when case-based planning is applied to lesson planning within a tutoring system.
An overview of approaches to qualitative model construction
Authors: Schut Cis , Bredeweg Bert 
Published: July 2009

AbstractIn qualitative reasoning research, much effort has been spent on developing representation and reasoning formalisms. Only recently, the process of constructing models in terms of these formalisms has been recognised as an important research topic of its own. Approaches addressing this topic are examined in this review. For this purpose a general model of the task of constructing qualitative models is developed that serves as a frame of reference in considering these approaches. Two categories of approaches are identified: model composition and model induction approaches. The former compose a model from predefined partial models and the latter infer a model from behavioural data. Similarities and differences between the approaches are discussed using the general task model as a reference. It appears that the majority of approaches focus on automating model construction entirely. Assessing and debugging a model in cooperation with a modeller is identified as an important topic for future research
Computer science research on scientific discovery
Authors: Valdés-Pérez Raúl E. 
Published: July 2009

AbstractThis article is an essay on directions and methodology in computer-science oriented research on scientific discovery. The essay starts by reviewing briefly some of the history of computing in scientific reasoning, and some of the results and impact that have been achieved. The remainder analyses some of the goals of this field, its relations with sister fields, and the practical applications of this analysis to evaluating research quality, reviewing, and methodology. An earlier review in this journal (Kocabas 1991b) analysed scientific discovery programs in terms of their designs, achievements and shortcomings; the focus here is research directions, evaluation and methodology, all from the viewpoint of computer science.
Ontologies: principles, methods and applications
Authors: Uschold Mike , Gruninger Michael 
Published: July 2009

AbstractThis paper is intended to serve as a comprehensive introduction to the emerging field concerned with the design and use of ontologies. We observe that disparate backgrounds, languages, tools and techniques are a major barrier to effective communication among people, organisations and/or software understanding (i.e. an “ontology”) in a given subject area, can improve such communication, which in turn, can give rise to greater reuse and sharing, inter-operability, and more reliable software. After motivating their need, we clarify just what ontologies are and what purpose they serve. We outline a methodology for developing and evaluating ontologies, first discussing informal techniques, concerning such issues as scoping, handling ambiguity, reaching agreement and producing definitions. We then consider the benefits and describe, a more formal approach. We re-visit the scoping phase, and discuss the role of formal languages and techniques in the specification, implementation and evalution of ontologies. Finally, we review the state of the art and practice in this emerging field, considering various case studies, software tools for ontology development, key research issues and future prospects.
Automated modelling: a discussion and review
Authors: Xia S. , Smith N. 
Published: July 2009

AbstractAutomated modelling is a young research field and is attracting increasingly more attention. It is a cross-disciplinary field involving simulation, modelling, qualitative reasoning, bond graphs and systems dynamics. It is an investigation of the modelling process with the purpose of developing computer tools which will automatically follow modelling principles. In addition, these tools will take into account the details of an application and generate the most appropriate model for the application. Its objective is to develop computer modelling tools which will have perception of model correctness, completeness and appropriateness and can perform modelling automatically. One way to achieve this objective is to introduce well-defined models and automate the process of assembling submodels into models to create well-defined models. This paper reviews the motivation and background behind this new field, its theory and current state of the art, compares existing approaches and discusses the underlying issues. It is hoped that more researchers will become aware of this field and be encouraged to work in it.
Review of pattern matching approaches
Authors: Manfaat D. , Duffy A. H. B. , Lee B. S. 
Published: July 2009

AbstractThis paper presents a review of pattern matching techniques. The application areas for pattern matching are extensive, ranging from CAD systems to chemical analysis and from manufacturing to image processing. Published techniques and methods are classified and assessed within the context of three key issues: pattern classes, similarity types and matching methods. It has been shown that the techniques and approaches are as diverse and varied as the applications.