bluuebunny/crossref_metadata_2025 huggingface dataset:
International Labour Organisation: problems with the supervisory machinery in 1974
Authors: de Vries Reilingh O.G.
Published: July 2009

The most remarkable event during the 59th session of the International Labour Conference in 1974 was undoubtedly the refusal to adopt the Report of the Committee on the Application of Conventions and Recommendations. This is all the more noteworthy when one realizes that this has never before happened in the history of the International Labour Organisation.

As a rule the Application Committee Report is adopted without taking a vote, despite the divergent views often expressed when the Report is discussed in the plenary session of the Conference. Only on very rare occasions has a vote been necessary (always resulting in adoption of the Report), while speakers sometimes simply indicated in the plenary discussion that they would abstain. Since the adoption of the Report of the Committee on the Application of Conventions and Recommendations is the yearly culmination in the regular supervision of on the implementation of ILO standards, the non-adoption of the Report in 1974 could have serious repercussions in the future for the whole regular supervisory system of the ILO and consequently for the development of international law.
Developments in disarmament law
Authors: Bos E.
Published: July 2009

During the reporting period, May 1973 - May 1974, discussions and negotiations in the field of arms control and disarmament continued in the different forums, but new agreements have not emerged from those deliberations so far. The purpose of this note is merely to highlight the trends perceptible in the negotiating bodies.
The status of consent in international law
Authors: Tammes A.J.P.
Published: July 2009

This study is concerned with the following question. To what extent, at the present stage of development of the international community, is individual consent required for an international person to be affected by changes in international law, and how far is his consent irrelevant or ineffective? As to new persons the first part of the question needs, of course, some adjustment so that it should be phrased: to what extent would the consent of the new person be required in order for him to be bound by international law existing at the time of his birth?
Reaffirmation and Development of international humanitarian law applicable in Armed Conflicts: the Conference of Government Experts, 24 May-12 June, 1971.
Authors: Kalshoven F.
Published: July 2009

When, on 12 August 1949, the Diplomatic Conference of Geneva finished its work, an important part of the law of armed conflict had been “codified and progressively developed” in the light of past experience both of the Second World War and other recent armed conflicts. However, this was a far cry from saying that one could lean back in satisfaction over a job well done; indeed, equally –or perhaps even more– important problems of the law of armed conflict had remained outside the scope of the Conference and, hence, had not found a solution in the four Conventions it had adopted. The Conference had, in fact, been restricted to an examination of the problems concerning protection of war victims rather than those concerning warfare proper; and, although the IVth or Civilians Convention in particular incidentally deals with the power of belligerents to attack enemy territory, the problem of the protection of the civil population against the dangers of modern warfare had remained essentially open.
Some thoughts on the future of the International Court of Justice
Authors: Petrén B.A.S.
Published: July 2009

For some years the future of the International Court of Justice has been a matter of growing concern to supporters of international adjudication as a way of settling legal disputes between States. This concern has also made itself felt inside the Organization of the United Nations, where the review of the role of the Court was a standing item on the agenda of the General Assembly for a number of years until the adoption on 22 November 1974 of resolution 3232 (XXIX). In this resolution the General Assembly inter alia expressed its satisfaction with the Court's 1972 revision of its Rules, and called upon States to have recourse to the Court more often.
Some Reflections on “Functional Sovereignty”
Authors: Riphagen W.
Published: July 2009

The concept of the sovereign State is still at the core of most rules of international law. It is true that both the individual human being and the international organisation have increasingly found recognition in rules of international law as (acting) “entities” which are more than mere “creatures” of the sovereign State or States. Nevertheless, progress in both directions is slow and does not, as yet, really undermine the international system of co-existing sovereign States.
The Common Market fisheries policy and the law of the sea
Authors: Van der Mensbrugghe Yves
Published: July 2009

Under Article 2 of the Treaty of Rome the European Economic Community “shall have as its task … to promote throughout the Community a harmonious development of economic activities, a continuous and balanced expansion, an increase in stability, an accelerated raising of the standard of living and closer relations between the member States belonging to it.” These purposes are to be achieved by establishing a Common Market and by progressively approximating the economic policies of the Member States.
There's no theory better than that of a practical tool
Authors: Musen Mark A.
Published: July 2009

This special issue of The Knowledge Engineering Review includes three papers on the subject of automated knowledge-acquisition tools. Gaines and Shaw (1993) review knowledge acquisition tools based on personal-construct psychology; Birmingham and Klinker (1993) review tools based on predefined models of problem-solving methods; and Eriksson and Musen (1993) review metalevel tools that developers may use to build other knowledge acquisition tools. Although this trio of papers by no means summarizes all recent developments in computer-based assistance for knowledge engineering, these contributions provide examples of three important research frontiers that, in many ways, trace their origins to early expert systems research.
Logic-based reasoning about actions and plans in artificial intelligence
Authors: Lee Huaming , Tannock James , Williams Jon Sims
Published: July 2009

Reasoning about actions and plans is a vital aspect of the rational behaviour of intelligent agents, and hence represents a major research domain in artificial intelligence. Much work has been undertaken to develop logic-based formalisms and problem solving procedures for plan representation and plan synthesis. This paper consists of a survey of various paradigms for reasoning about actions and plans in artificial intelligence. Attention is focused on the logic-based theoretical frameworks which have built a formal foundation for the domain-independent approaches to the general principles of reasoning about actions and plans.
State-of-the-art report on reactive processing in databases and artificial intelligence
Authors: Bayer Petra
Published: July 2009

Active rules have been a standard technique in artificial intelligence (AI) for almost two decades. Variants of the Al methods are currently being adapted to provide database systems with the ability to respond reactively to events and database state changes. This paper gives an overview of developments in reactive processing database research, concentrating on active databases that are integrated with relational and object-oriented systems. A general presentation of “trigger”-based processing techniques is given, with a detailed review of active relational and object-oriented database models. An overview of technologies for active processing in AI is also presented, and some common and contrasting themes in database and AI technology identified.
Commitments and conventions: The foundation of coordination in multi-agent systems
Authors: Jennings Nick R.
Published: July 2009

Distributed Artificial Intelligence systems, in which multiple agents interact to improve their individual performance and to enhance the systems' overall utility, are becoming an increasingly pervasive means of conceptualising a diverse range of applications. As the discipline matures, researchers are beginning to strive for the underlying theories and principles which guide the central processes of coordination and cooperation. Here agent communities are modelled using a distributed goal search formalism, and it is argued thatcommitments(pledges to undertake a specific course of action) andconventions(means of monitoring commitments in changing circumstances) are the foundation of coordination in multi-agent systems. An analysis of existing coordination models which use concepts akin to commitments and conventions is undertaken before a new unifying framework is presented. Finally, a number of prominent coordination techniques which do notexplicitlyinvolve commitments or conventions are reformulated in these terms to demonstrate their compliance with the central hypothesis of this paper.
Introducing expert critiquing systems
Authors: Hägglund Sture
Published: July 2009

Expert critiquing systems present an interesting and important complement to conventional expert systems. The basic idea is to emphasize the support for the user's own decision making rather than to let the system independently suggest a solution to a given problem. Thus it is assumed that the user initially proposes a decision or course of action. The system then reviews this suggestion relative to known circumstance, tries to evaluate the proposed solution, provides suggestions for improvements, draws attention to possible risks, indicates alternatives, and evaluates their merits, etc. A critiquing system is characterized both by its approach to problem solving and by its style of interaction with the user, as will be detailed below.
Embedding critics in design environments
Authors: Fischer Gerhard , Nakakoji Kumiyo , Ostwald Jonathan , Stahl Gerry , Sumner Tamara
Published: July 2009

Human understanding in design evolves through a process of critiquing existing knowledge and consequently expanding the store of design knowledge. Critiquing is a dialogue in which the interjection of a reasoned opinion about a product or action triggers further reflection on or changes to the artifact being designed. Our work has focused on applying this successful human critiquing paradigm to human-computer interaction. We argue that computer-based critiquing systems are most effective when they are embedded in domain-oriented design environments, which are knowledge-based computer systems that support designers in specifying a problem and constructing a solution. Embedded critics play a number of important roles in such design environments: (1) they increase the designer's understanding of design situations by pointing out problematic situations early in the design process; (2) they support the integration of problem framing and problem solving by providing a linkage between the design specification and the design construction; and (3) they help designers access relevant information in the large information spaces provided by the design environment. Three embedded critiquing mechanisms—generic, specific, and interpretive critics—are presented, and their complementary roles within the design environment architecture are described.
Engineering experts critics for cooperative systems
Authors: Silverman Barry G. , Wenig R. Gregory
Published: July 2009

Knowledge 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”.
Logic Programming in the Fifth Generation
Authors: Kowalski Robert
Published: July 2009

The Japanese Fifth Generation Computer Systems (FGCS) project has chosen logic programming for its core programming language. It has recognized the major contribution that logic programming has to make not only in artificial intelligence but in database systems and software specification as well. It has recognized and intends to exploit the greater potential that logic programming has to offer for taking advantage of the parallelism possible with innovative multiprocessor computer architectures.
A Review of knowledge-based planning techniques
Authors: Tate Austin
Published: July 2009

Planning systems have been an active research topic within Artificial Intelligence for over two decades. There have been a number of techniques developed during that period which still form an essential part of many of today's planners. This paper introduces the techniques, attempts to classify some of the important research themes in AI planning and describes their historical development.
Knowledge-based expert systems — The State of the Art in the US
Authors: Hayes-Roth F.
Published: July 2009

This 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.
Types of Control Structure in Expert Systems
Authors: Laurent Jean-Pierre
Published: July 2009

The concept of an expert system covers an increasingly large group of software packages which often have more dissimilarities than points in common. We shall not attempt to give a precise definition of an expert system here, because this might impose too restrictive a framework on the rest of our discussion. We shall simply state that, as is generally recognized, an expert system is a piece of software intended to resolve a certain category of problems, that it uses for this purpose a large quantity of knowledge specific to the field in question, and that in each expert system there is a very distinct separation between this knowledge and the procedures which make use of it.
Criteria for Choosing Representation Languages and Control Regimes for Expert Systems
Authors: Reichgelt Han , van Harmelen Frank
Published: July 2009

Shells 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

Cognitive 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

For 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

This 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

Knowledge-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.
Expert systems in UK business: a critical assessment
Authors: D'Agapeyeff A. , Hawkins C.J.B.
Published: July 2009

The main purpose of this survey was to update a survey report prepared for the UK Alvey Directorate during 1984. The update was focused on business, and had to be qualitative because the increased confidentiality associated with the majority of projects precluded a quantitative analysis, and is confined to a synthesis of the impressions we have obtained. The survey was not intended to be representative. On the contrary, organizations were identified as being amongst those most likely to provide interesting application experience. This led to an arbitrary selection. For example, all banks were excluded because it was felt they were not yet ready to talk of their experiences. The companies approached consisted of five organizations visited in 1983, five further user companies and five service companies (e.g. consultants and software houses).
Expert systems in business: a Japanese experience
Authors: Mori Fumihiko
Published: July 2009

This paper gives the personal view of a Japanese knowledge engineer, concerning the present status of the evolution of knowledge engineering in Japan, especially its applications in the business field, user evaluation and acceptance, problems, and future prospects.
Learning to use the S.1 knowledge engineering tool
Authors: Ward R.D. , Sleeman D.
Published: July 2009

It 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

This 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.
The nature of expertise and its elicitation for business expert systems: a commentary
Authors: D'agapeyeff A.
Published: July 2009

The knowledge engineering practices of one experienced company are outlined, commenting on differences in emphasis in some of the knowledge elicitation and AI research literatures.
Expert systems and hypertext
Authors: Rada Roy , Barlow Judith
Published: July 2009

Expert 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

AI 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

Progress 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

First-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

This 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

The 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

An 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

Mechanical 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

This 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

There 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

This 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

Intelligent 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

Learning 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

Computational 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

In 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.
Qualitative reasoning overtime: history and current prospects
Authors: Travé-Massuyès Louise
Published: July 2009

This paper provides a historical summary of the motivations which have led several research communities to contemplate qualitative techniques. Qualitative reasoning satisfies various problem solving needs in high level decision tasks, embodied in a set of tools which allow deep knowledge to be put in compatible form with software requirements while still remaining realistic. An overview of these mathematical formalisms is presented; qualitative simulation is introduced as one of the most significant outcomes. Finally, some current research issues concerning temporal aspects of qualitative reasoning are discussed.
From knowledge bases to decision models
Authors: Wellman Michael P. , Breese John S. , Goldman Robert P.
Published: July 2009

In recent years there has been a growing interest among AI researchers in probabilistic and decision modelling, spurred by significant advances in representation and computation with network modelling formalisms. In applying these techniques to decision support tasks, fixed network models have proven to be inadequately expressive when a broad range of situations must be handled. Hence many researchers have sought to combine the strengths of flexible knowledge representation languages with the normative status and well-understood computational properties of decision-modelling formalisms and algorithms. One approach is to encode general knowledge in an expressive language, then dynamically construct a decision model for each particular situation or problem instance. We have developed several systems adopting this approach, which illustrate a variety of interesting techniques and design issues.
The qualitative representation of physical systems
Authors: Coiera Enrico
Published: July 2009

The representation of physical systems using qualitative formalisms is examined in this review, with an emphasis on recent developments in the area. The push to develop reasoning systems incorporating deep knowledge originally focused on naive physical representations, but has now shifted to more formal ones based on qualitative mathematics. The qualitative differential constraint formalism used in systems like QSIM is examined, and current efforts to link this to competing representations like Qualitative Process Theory are noted. Inference and representation are intertwined, and the decision to represent notions like causality explicitly, or infer it from other properties, has shifted as the field has developed. The evolution of causal and functional representations is thus examined. Finally, a growing body of work that allows reasoning systems to utilize multiple representations of a system is identified. Dimensions along which multiple model hierarchies could be constructed are examined, including mode of behaviour, granularity, ontology, and representational depth.
Principles and practice in verifying rule-based systems
Authors: Preece Alun D. , Shinghal Rajjan , Batarekh Aïda
Published: July 2009

This paper surveys the verification of expert system knowledge bases by detecting anomalies. Such anomalies are highly indicative of errors in the knowledge base. The paper is in two parts. The first part describes four types of anomaly: redundancy, ambivalence, circularity, and deficiency. We consider rule bases which are based on first-order logic, and explain the anomalies in terms of the syntax and semantics of logic. The second part presents a review of five programs which have been built to detect various subsets of the anomalies. The four anomalies provide a framework for comparing the capabilities of the five tools, and we highlight the strengths and weaknesses of each approach. This paper therefore provides not only a set of underlying principles for performing knowledge base verification through anomaly detection, but also a survey of the state-of-the-art in building practical tools for carrying out such verification. The reader of this paper is expected to be familiar with first-order logic.
A unifying framework for concept-learning algorithms
Authors: de Raedt Luc , Bruynooghe Maurice
Published: July 2009

A unifying framework for concept-learning, derived from Mitchell's Generalization as Search-paradigm, is presented. Central to the framework is the generic algorithm Gencol. Gencol forms a synthesis of existing concept-learning algorithms as it identifies the key issues in concept-learning: the representation of concepts and examples, the search strategy and heuristics, and the operators that transform one concept-description into another one when searching the concept description space. Gencol is relevant for practical purposes as it offers a solid basis for the design and implementation of concept-learning algorithms. The presented framework is quite general as seemingly disparate algorithms such as TDIDT, AQ, MIS and version spaces fit into Gencol.
Knowledge-Based Software Engineering
Authors: Johnson W. Lewis
Published: July 2009

The 7th Annual Knowledge-Based Software Engineering Conference was held at the McLean Hilton at Tysons Comer, in McLean, Virginia, on Sept. 20–23, 1992. This conference was sponsored by Rome Laboratory and held in cooperation with the IEEE Computer Society, ACM SIGART and SIGSOFT, and the American Association for Artificial Intelligence (AAAI).
Artificial intelligence in business II: Development, integration and organizational issues
Authors: O'Leary Daniel E. , Kingston John
Published: July 2009

The purpose of this paper is to review the use of knowledge-based systems and artificial intelligence (AI) in business. Part I of this paper provided a broad survey of the use of AI in business, summarizing the application of AI in a number of business domains. In addition, it also provided a summary of the use of different forms of knowledge representation in business applications. Part I has a large set of references, including a number of survey papers, focusing on AI in business. Part II of this paper consists of more detailed analysis of particular systems or issues affecting AI in business. It examines technical issues which are central to the construction of business AI systems, and it also examines the commercial contribution made by methods for the development of AI systems. In addition, part II looks at integration between AI and more traditional information systems. AI can be used to add value to many existing information systems, such as database management systems. Particular attention is given to the integration of AI with operations research, which is the one of the primary “competitors” of AI, providing an alternative set of support tools for decision making.

Business organizations are not concerned only with technology issues; there is also concern about the impact of AI on organizations. Further, the evaluation of AI often is based on an economic view of the world. Part II therefore investigates the organizational impact of AI, and the economics of AI, including issues such as value creation. The format of Part II is as follows: Section 8 analyses techniques for improving the performance of AI systems, thus maximizing economic return. Section 9 looks at different forms of uncertainty and ambiguity which must be dealt with by AI systems. It examines the contributions of fuzzy logic and numerical measures of certainty to handling these problems. Section 10 examines the usefulness of different approaches to knowledge acquisition in business situations, and investigates the benefits of methodological approaches to AI applications. It also looks at more recent AI programming techniques which eliminate the need for knowledge elicitation from an expert: neural networks, case-based reasoning and genetic algorithms are discussed. Sections 11 and 12 examine issues of integrating AI systems. Generally, the use of AI in business settings must ultimately be integrated with the broader base of corporate information systems. Section 11 looks at integration with information systems in general, and section 12 looks particularly at integration with operations research. Sections 13 and 14 review the organizational and economic impact of AI. Finally, section 15 provides a brief summary of part II.
Knowledge discovery in databases: Progress report
Authors: Piatetsky-Shapiro Gregory
Published: July 2009

As the number and size of very large databases continues to grow rapidly, so does the need to make sense of them. This need is addressed by the field called knowledge Discovery in Databases (KDD), which combines approaches from machine learning, statistics, intelligent databases, and knowledge acquisition. KDD encompasses a number of different discovery methods, such as clustering, data summarization, learning classification rules, finding dependency networks, analysing changes, and detecting anomalies (Matheus et at., 1993).
Uncertainty in artificial intelligence
Authors: Parsons Simon
Published: July 2009

The first conference on Uncertainty in Artificial Intelligence was held in 1985 by a group of people who felt that their views on the use of probability theory were not receiving a fair hearing from the rest of the Al community. At the time, mainstream opinion held that computational complexity of, and the amount of data required by, probabilistic methods made them inappropriate for realistic applications. As a result, those who claimed that probability theory was an adequate, if not the only adequate, method of handling uncertainty received a somewhat frosty reception.