This 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.

Agent software is a rapidly developing area of research. However, the overuse of the word “agent” has tended to mask the fact that, in reality, there is a truly heterogeneous body of research being carried out under this banner. This overview paper presents a typology of agents. Next, it places agents in context, defines them and then goes on, inter alia, to overview critically the rationales, hypotheses, goals, challenges and state-of-the-art demonstrators of the various agent types in our typology. Hence, it attempts to make explicit much of what is usually implicit in the agents literature. It also proceeds to overview some other general issues which pertain to all the types of agents in the typology. This paper largely reviews software agents, and it also contains some strong opinions that are not necessarily widely accepted by the agent community.

In 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

Automated 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.

The basic nearest neighbour algorithm works by storing the training instances and classifying a new case by predicting that it has the same class as its nearest stored instance. To measure the distance between instances, some distance metric needs to be used. In situations when all attributes have numeric values, the conventional nearest neighbour method treats examples as points in feature spaces and uses Euclidean distance as the distance metric. In tasks with only nominal attributes, the simple “over-lap” metric is usually used. To handle classification tasks that have mixed types of attributes, the two different metrics are simply combined. Work by researchers in the machine learning field has shown that this approach performs poorly. This paper attempts to study a more recently developed distance metric and show that this metric is capable of measuring the importance of different attributes. With the use of discretisation for numeric-valued attributes, this method provides an integrated way in dealing with problem domains with mixtures of attribute types. Through detailed analyses, this paper tries to provide further insights into the understanding of nearest neighbour classification techniques and promote further use of this type of classification algorithm.

Software process modelling is the activity of formalising the production lifecycle of large software systems. Its aim is to formally describe a software development process, which is then effectively used and possibly enacted by an environment able to support the geographically distributed and coordinated activities involved in the process itself. I show that rule-based languages, especially logic programming languages, are an important technology for the specification, modelling, enactment and coordination of software processes. This is because most routine activities in any development process can be defined by rules. Some initial proposals aimed at simply simulating the software process by a Prolog-like program embedding some development rules. A further step toward the integration of rule-based languages in the software process has been taken using a dynamic knowledge base as project database, and a number of special primitives have been introduced to support process programs. Currently there is a trend toward more complex programming environments, called process-centred development environments. I show how some rule-based coordination language have been used to build an environment of this kind.

This paper reviews a number of applications of machine learning to industrial control problems. We take the point of view of trying to automatically build rule-based reactive systems for tasks that, if performed by humans, would require a high degree of skill, yet are generally performed without thinking. Such skills are said to be sub-cognitive. While this might seem restrictive, most human skill is executed subconsciously and only becomes conscious when an unfamiliar circumstance is encountered. This kind of skill lends itself well to representation by a reactive system, that is, one that does not create a detailed model of the world, but rather, attempts to match percepts with actions in a very direct manner.

TFL, the Task Formal Language, has been developed for integrating the static and dynamic aspects of knowledge based systems. This paper focuses on the formal specification of dynamic behaviour. Although fundamental in knowledge based systems, strategic reasoning has been rather neglected until now by the existing formal specifications. Most languages were generally more focused on the domain and problem-solving knowledge specification than on the control. The formalisation presented here differs from previous ones in several aspects. First, a different representation of dynamic knowledge is proposed: TFL is based on Algebraic Data Types, as opposed to dynamic or temporal logic. Second, dynamic strategic reasoning is emphasised, whereas existing languages only offer to specify algorithmic control. Then, TFL does not only provide the specification of the problem-solving knowledge of the object system, but also of its strategic knowledge. Finally, the dynamic knowledge of the meta-system itself is also specified. Moreover, modularisation is another important feature of the presented language.

Deriving formal specifications from informal requirements is extremely difficult since one has to overcome the conceptual gap between an application domain and the domain of formal specification methods. To reduce this gap we introduce application-specific specification languages, i.e., graphical and textual notations that can be unambiguously mapped to formal specifications in a logic language. We describe a number of realised approaches based on this idea, and evaluate them with respect to their domain specificity vs. generality.

In recent years a number of programming languages based upon the direct execution of temporal logic formulae have been developed. The use of such logics provides a powerful basis for the representation and implementation of a range of dynamic behaviours. Though many of these languages are still experimental, they are beginning to be applied, not only in computer science and AI, but also in less obvious areas such as user interfaces, process control and social modelling. This article provides an introduction to some of the basic concepts of executable temporal logics, together with an overview of the main approaches being pursued.

This 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.

Time is one of the most relevant topics in AI. It plays a major role in several of AI research areas, ranging from logical foundations to applications of knowledge-based systems. Despite the ubiquity of time in AI, researchers tend to specialise and focus on time in particular contexts or applications, overlooking meaningful connections between different areas. In an attempt to promote crossfertilisation and reduce isolation, the Temporal Representation and Reasoning (TIME) workshop series was started in 1994. The third edition of the workshop was held on May 19–20 1996 in Key West, FL, with S. D. Goodwin and H. J. Hamilton as General Chairs, and L. Chittaro and A. Montanari as Program Chairs. A particular emphasis was given to the foundational aspects of temporal representation and reasoning through an investigation of the relationships between different approaches to temporal issues in AI, computer science and logic.

This 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.

For the first time, post-conference workshops were organised for the International Conference on Deductive and Object-Oriented Databases (DOOD). There were two workshops focusing on knowledge discovery and temporal reasoning. This report is dedicated to one dealing with temporal reasoning.

Logic programming is a programming paradigm with potential to contribute to software engineering. This paper is concerned with one dimension of that potential, the impact that experience with developing logic programs can have on software design. We present a logic programming perspective on programming patterns, systematic program development, design for provability, and the paradigm of meta-programming.

In his thought-provoking paper, Valdes-Perez (1996, this volume) carefully describes the methodology and future directions for research in Machine Scientific Discovery (MSD), as viewed predominantly from the standpoint of computer science. Here, I shall offer some remarks from the angle of domain sciences (despite the fact that my basic area of expertise is actually general and computational linguistics).

The KEML (Knowledge Engineering Methods and Languages) workshop which took place in a suburb of Paris was the sixth in a series, and was preceded by workshops in Bonn (1991 and 1993), Karlsruhe (1992) and Amsterdam (1994 and 1995). Originally, the workshops were dedicated to formal specification languages for KADS models, but later the scope was widened to include methods and languages for Knowledge Engineering in general. However, the emphasis of the KEML workshops remains on formal languages and techniques, and this distinguishes it from the more general Knowledge Acquisition workshops such as KAW (Banff, Canada), EKAW (Europe) and JKAW (Japan).

The complex process of deriving programs from specifications is often divided into the following three steps: (i) the derivation of formal specifications from the informal ones; (ii) the validation of the formal specifications; and (iii) the derivation of executable programs from the formal specifications.

The Third International Workshop on Deontic Logic in Computer Science (ΔEON'96) took place in Sesimbra, Portugal, from 11–13 January 1996. It consisted of 12 refereed technical presentations and four invited talks. The invited speakers were Nuel Belnap (Pittsburgh University, USA), Andrew Jones (Oslo University, Norway), Krister Segerberg (Uppsala University, Sweden) and Marek Sergot (Imperial College, UK).