Recent efforts in spatial and temporal data models and database systems have attempted to achieve an appropriate kind of interaction between the two areas. This paper reviews the different types of spatio-temporal data models that have been proposed in the literature as well as new theories and concepts that have emerged. It provides an overview of previous achievements within the domain and critically evaluates the various approaches through the use of a case study and the construction of a comparison framework. This comparative review is followed by a comprehensive description of the new lines of research that emanate from the latest efforts inside the spatio-temporal research community.

Pervasive computing is by its nature open and extensible, and must integrate the information from a diverse range of sources. This leads to a problem of information exchange, so sub-systems must agree on shared representations. Ontologies potentially provide a well-founded mechanism for the representation and exchange of such structured information. A number of ontologies have been developed specifically for use in pervasive computing, none of which appears to cover adequately the space of concerns applicable to application designers. We compare and contrast the most popular ontologies, evaluating them against the system challenges generally recognized within the pervasive computing community. We identify a number of deficiencies that must be addressed in order to apply the ontological techniques successfully to next-generation pervasive systems.

We think of match as an operator that takes two graph-like structures (e.g. database schemas or ontologies) and produces a mapping between elements of the two graphs that correspond semantically to each other. The goal of this paper is to propose a new approach to matching, called semantic matching. As its name indicates, in semantic matching the key intuition is to exploit the model-theoretic information, which is codified in the nodes and the structure of graphs. The contributions of this paper are (i) a rational reconstruction of the major matching problems and their articulation in terms of the more generic problem of matching graphs, (ii) the identification of semantic matching as a new approach for performing generic matching and (iii) a proposal for implementing semantic matching by testing propositional satisfiability.

This article reviews research on how people use and understand linguistic expressions of uncertainty, with a view toward the needs of researchers and others interested in artificial intelligence systems. We discuss and present empirical results within an inductively developed theoretical framework consisting of two background assumptions and six principles describing the underlying cognitive processes.

This commentary summarizes case-based reasoning research applied in the medical domain. In this commentary the term ‘medical’ is used in an all-encompassing manner. It comprises all aspects of health, for example, from diagnosis to nutrition planning. This article provides references to researchers in the field, systems, workshops, and landmark publications.

An appropriate use of neural computing techniques is to apply them to problems such as condition monitoring, fault diagnosis, control and sensing, where conventional solutions can be hard to obtain. However, when neural computing techniques are used, it is important that they are employed so as to maximise their performance, and improve their reliability. Their performance is typically assessed in terms of their ability to generalise to a previously unseen test set, although unless the training set is very carefully chosen, 100% accuracy is rarely achieved. Improved performance can result when sets of neural nets are combined in ensembles and ensembles can be viewed as an example of the reliability through redundancy approach that is recommended for conventional software and hardware in safety-critical or safety-related applications. Although there has been recent interest in the use of neural net ensembles, such techniques have yet to be applied to the tasks of condition monitoring and fault diagnosis. In this paper, we focus on the benefits of techniques which promote diversity amongst the members of an ensemble, such that there is a minimum number of coincident failures. The concept of ensemble diversity is considered in some detail, and a hierarchy of four levels of diversity is presented. This hierarchy is then used in the description of the application of ensemble-based techniques to the case study of fault diagnosis of a diesel engine.

Upon assessing probabilities for Bayesian belief networks, the knowledge and practical experience of experts is often the only available source of probabilistic information. It is important to realise that issues concerning human capabilities with respect to making judgements come into play when relying on experts for probability elicitation. A number of methods for the elicitation of probabilities are known from the field of decision analysis. These methods try, to some extent, to deal with those issues. I present here an overview of the issues to consider when relying on expert judgements and describe the methods that are available for expert elicitation, along with their benefits and drawbacks.

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.

We address the problem of highly varied and inconsistent usage of terms by the knowledge technology community in the area of knowledge-level modelling. It is arguably difficult or impossible for any standard set of terms and definitions to be agreed on. However, de facto standard usage is already emerging within and across certain segments of the community. This is very difficult to see, however, especially for newcomers to the field. It is the goal of this paper to identify and reflect the most common usage of terms as currently found in the literature. To this end, we introduce and define the concept of a knowledge level model, comparing how the term is used today with Newell's original usage. We distinguish two major types of knowledge level model: ontologies and problem solving models. We describe what an ontology is, what they may be used for and how they are represented. We distinguish various kinds of ontologies and define a number of additional related concepts. We describe what is meant by a problem solving model, what they are used for, and attempt to clarify some terminological confusion that exists in the literature. We define what is meant by the term ‘problem’, and some common notions used to characterise and represent problems. We introduce and describe the ideas of tasks, problem solving methods and a variety of other important related concepts.

This paper sets out, ambitiously, to present a brief reappraisal of software agents research. Evidently, software agent technology has promised much. However, some five years after the word ‘agent’ came into vogue in the popular computing press, it is perhaps time the efforts in this fledgling area are thoroughly evaluated with a view to refocusing future efforts. We do not pretend to have done this in this paper—but we hope we have sown the first seeds towards a thorough first five-year report on the software agents area. The paper contains some strong views not necessarily widely accepted by the agent community.The views expressed in this paper are those of the authors only, and do not necessarily reflect the views of our organisation.

Ontology evolution aims at maintaining an ontology up to date with respect to changes in the domain that it models or novel requirements of information systems that it enables. The recent industrial adoption of Semantic Web techniques, which rely on ontologies, has led to the increased importance of the ontology evolution research. Typical approaches to ontology evolution are designed as multiple-stage processes combining techniques from a variety of fields (e.g., natural language processing and reasoning). However, the few existing surveys on this topic lack an in-depth analysis of the various stages of the ontology evolution process. This survey extends the literature by adopting a process-centric view of ontology evolution. Accordingly, we first provide an overall process model synthesized from an overview of the existing models in the literature. Then we survey the major approaches to each of the steps in this process and conclude on future challenges for techniques aiming to solve that particular stage.

This literature review focuses on user-centered evaluation (UCE) studies of adaptive and adaptable systems. Usability, perceived usefulness and appropriateness of adaptation are the three most commonly assessed variables. Questionnaires appeared to be the most popular method, followed by interviews and data log analysis. The quality of most questionnaires was questionable, and the reporting of interviews and think-aloud protocols was found to be shallow. Furthermore, data logs need triangulation in order to be useful. The reports encountered lack empirical value. The article models the iterative design process for adaptive and adaptable systems, linked to the goals of UCE: supporting decisions, detecting problems and verifying quality. This model summarizes the variables to be assessed at each stage of the process and the relevant methods to assess them.

Knowledge acquisition research supports the generation of knowledge-based systems through the development of principles, techniques, methodologies and tools. What differentiates knowledge-based system development from conventional system development is the emphasis on in-depth understanding and formalization of the relations between the conceptual structures underlying expert performance and the computational structures capable of emulating that performance.

Personal construct psychology is a theory of individual and group psychological and social processes that has been used extensively in knowledge acquisition research to model the cognitive processes of human experts. The psychology takes a constructivist position appropriate to the modelling of human knowledge processes, but develops this through the characterization of human conceptual structures in axiomatic terms that translate directly to computational form. In particular, there is a close correspondence between the intensional logics of knowledge, belief and action developed in personal construct psychology, and the intensional logics for formal knowledge representation developed in artificial intelligence research as term subsumption, or KL-ONE-like, systems.

This paper gives an overview of personal construct psychology and its expression as an intensional logic describing the cognitive processes of anticipatory agents, and uses this to survey knowledge acquisition tools deriving from personal construct psychology.

A large variety of issues influence the success of data mining on a given problem. Two primary and important issues are the representation and the quality of the dataset. Specifically, if much redundant and unrelated or noisy and unreliable information is presented, then knowledge discovery becomes a very difficult problem. It is well-known that data preparation steps require significant processing time in machine learning tasks. It would be very helpful and quite useful if there were various preprocessing algorithms with the same reliable and effective performance across all datasets, but this is impossible. To this end, we present the most well-known and widely used up-to-date algorithms for each step of data preprocessing in the framework of predictive data mining.

Planning research in Artificial Intelligence (AI) has often focused on problems where there are cascading levels of action choice and complex interactions between actions. In contrast, scheduling research has focused on much larger problems where there is little action choice, but the resulting ordering problem is hard. In this paper, we give an overview of AI planning and scheduling techniques, focusing on their similarities, differences, and limitations. We also argue that many difficult practical problems lie somewhere between planning and scheduling, and that neither area has the right set of tools for solving these vexing problems.

Ontologies are becoming increasingly important because they provide the critical semantic foundation for many rapidly expanding technologies such as software agents, e-commerce and knowledge management (McGuinness, 2002). The Unified Modelling Language (UML)1 has been widely adopted by the software engineering community and its scope is broadening to include more diverse modelling tasks. This paper discusses the recent convergence of UML and ontologies and suggests some possible future directions.

In recent years there have been some efforts to automate the ontology acquisition and construction process. The proposed systems differ from each other in some factors and have many features in common. This paper presents the state of the art in Ontology Learning (OL) and introduces a framework for classifying and comparing OL systems. The dimensions of the framework concern what to learn, from where to learn it and how it may be learnt. They include features of the input, the methods of learning and knowledge acquisition, the elements learned, the resulting ontology and also the evaluation process. To extract this framework, over 50 OL systems or modules thereof that have been described in recent articles are studied here and seven prominent ones, which illustrate the greatest differences, are selected for analysis according to our framework. In this paper after a brief description of the seven selected systems we describe the dimensions of the framework. Then we place the representative ontology learning systems into our framework. Finally, we describe the differences, strengths and weaknesses of various values for our dimensions in order to present a guideline for researchers to choose the appropriate features to create or use an OL system for their own domain or application.

Cooperation is often presented as one of the key concepts which differentiates multi-agent systems from other related disciplines such as distributed computing, object-oriented systems, and expert systems. However, it is a concept whose precise usage in agent-based systems is at best unclear and at worst highly inconsistent. Given the centrality of the issue, and the different ideological viewpoints on the subject, this was a lively panel which dealt with the following main issues.

Modelling and simulation have long been dominated by equation-based approaches, until the recent advent of agent-based approaches. To curb the resulting complexity of models, Axelrod promoted the KISS principle: ‘Keep It Simple, Stupid’. But the community is divided and a new principle appeared: KIDS, ‘Keep It Descriptive, Stupid’. Richer models were thus developed for a variety of phenomena, while agent cognition still tends to be modelled with simple reactive particle-like agents. This is not always appropriate, in particular in the social sciences trying to account for the complexity of human behaviour. One solution is to model humans as belief, desire and intention (BDI) agents, an expressive paradigm using concepts from folk psychology, making it easier for modellers and users to understand the simulation. This paper provides a methodological guide to the use of BDI agents in social simulations, and an overview of existing methodologies and tools for using them.