The relentless growth of the computer industry over more than 30 years has been driven on by a series of major innovations. High-level languages; solid-state logic; compatible machine ranges; disk storage; time-sharing; data communications; virtual machine architectures; the use of LSI and solid-state memory; text processing; personal computers; sucessful packaged software; each advance in its turn has opened up new markets and set off a new spurt of expansion. A vital conclusion of this study is that expert systems promise to be another such major innovation.

In this paper, we discuss the potential role of answer set programming (ASP) in the context of approaches to the development of agents and multi-agent systems especially in the realm of Computational Logic. After shortly recalling the main (computational-logic-based) agent-oriented frameworks, we introduce ASP; then, we discuss the usefulness of a potential integration of the two paradigms in a modular heterogeneous framework, and the feasibility of such integration. This also in the more general view of improving and empowering flexibility of agent-oriented frameworks. Relevant literature will be mentioned and discussed. Possible future directions and potential developments will be outlined.

To foster effective use of artificial intelligence planning and scheduling (P&S) systems in the real world, it is of great importance to both (a) broaden direct access to the technology for the end users and (b) significantly increase their trust in such technology. Automated P&S systems often bring solutions to the users that are neither ‘obvious’ nor immediately acceptable to them. This is because these tools directly reason on causal, temporal, and resource constraints; moreover, they employ resolution processes designed to optimize the solution with respect to non-trivial evaluation functions. Knowledge engineering environments aim at simplifying direct access to the technology for people other than the original system designers, while the integration of validation and verification (V&V) capabilities in such environments may potentially enhance the users’ trust in the technology. Somehow, V&V techniques may represent a complementary technology, with respect to P&S, that contributes to developing richer software environments to synthesize a new generation of robust problem-solving applications. The integration of V&V and P&S techniques in a knowledge engineering environment is the topic of this paper. In particular, it analyzes the use of state-of-the-art V&V technology to support knowledge engineering for a timeline-based planning system called MrSPOCK. The paper presents the application domain for which the automated solver has been developed, introduces the timeline-based planning ideas, and then describes the different possibilities to apply V&V to planning. Hence, it continues by describing the step of adding V&V functionalities around the specialized planner, MrSPOCK. New functionalities have been added to perform both model validation and plan verification. Lastly, a specific section describes the benefits as well as the performance of such functionalities.

Rough set theory (RST) has enjoyed an enormous amount of attention in recent years and has been applied to many real-world problems including data mining, pattern recognition, and intelligent control. Much research has recently been carried out in respect of both the development of the underlying theory and the application to new problem domains. This paper attempts to summarize the advances in RST, its extensions, and their applications. It also identifies important areas which require further investigation. Typical example application domains are examined which demonstrate the success of the application of RST to a wide variety of areas and disciplines, and which also exhibit the strengths and limitations of the respective underlying approaches.

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.

Several Apriori algorithm implementations for mining association rules have been proposed in the literature using the Hadoop-MapReduce framework and, more recently, Spark. However, none of the works have made a detailed assessment of its performance, for example, comparing it with other implementations in various characteristics of data sets. In this work, we present a review of the main algorithms proposed for Hadoop-MapReduce and compared their implementations in a single environment under several different situations. Moreover, these algorithms had their implementations adapted to Spark, and also compared under the same circumstances. Based on the results of the experiments, we present a framework for recommending the Apriori implementation most appropriate for solving a given problem, according to the data set characteristics and minimum required support. The results show that Spark implementations overcome Hadoop-MapReduce implementations at runtime in most experiments. However, there is no single implementation that is the best in all the evaluated situations.

We are delighted to present this special issue of The Knowledge Engineering Review, as it marks a significant accomplishment of the case-based reasoning (CBR) community. Its 19 commentaries, written by 41 authors, represent a compendium on the state-of-the-art in CBR. These evolved from a 2003 workshop that was held at Waiheke Island and Queenstown, New Zealand and chaired by Alec Holt and Ian Watson. The workshop's delegates identified the primary topics of CBR research and application, selected representative influential publications for each topic, and were encouraged to co-author commentaries on each topic with other CBR experts who were unable to attend. These collaborations produced the articles you now see. While several reviews exist on CBR (e.g. Marir & Watson, 1994; López de Mántaras & Plaza, 1997; Lenz et al., 1998), few have been published recently or have similar historical and subject breadth.

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.

The purpose of this paper is to evaluate and compare three of the most powerful expert system tools available: KEE from Intellicorp, Knowledge Craft from The Carnegie Group Inc., and ART from Inference Corporation.

These three tools are industrial development environments which are fully supported and well beyond research prototypes. They were implemented on Lisp machines initially, but will soon be available on conventional computers. The three systems are very flexible and offer many ways of representing knowledge.

The first part of the paper is a technical overview of each tool. The second part presents their common features. The third part discusses the advantages and drawbacks of each, according to types of application.

This work has been partially sponsored by the Commission of the European communities within the Esprit Project 932 entitled ‘Knowledged-based Real-Time supervision in Computer Integrated Manufacturing’. We are grateful to Didier Gaget-Soufflot (Graphael, France) and Bernard Raust (CGE/TITN) for their participation in the evaluation of KEE and ART respectivily.

Belief revision is the process of rearranging a knowledge base to preserve global consistency whilst accommodating incoming information. Early approaches to belief revision used symbolic model theoretic methods, considering the problem as one of changi ng a logical theory. More recent approaches have adopted qualitative syntactic methods, taking them into the area of truth maintenance systems, and numerical mathematical methods, thus moving into the mainstream literature of uncertainty management. Multi -agent systems, in which information may come from a variety of human and artificial sources with different degrees of reliability, seem to be a natural domain for belief revision. The aim of this paper is to give a synoptic perspective of this composite subject from the clear air of the high theoretical peaks down to the muddy plain of practical algorithms.

Knowledge-Based (KB) technology is being applied to complex problem solving and safety and business critical tasks in many application domains. Concerns have naturally arisen as to the dependability of Knowledge-Based Systems (KBS). As with any software, attention to quality and safety must be paid throughout development of a KBS, and rigorous Verification and Validation (V&V) techniques must be employed. Research in V&V of KBSs has emerged as a distinct field only in the last decade, and is intended to address issues associated with quality and safety aspects of KBSs, and to provide such applications with the same degree of dependability as conventional applications. In recent years, V&V of KBSs has been the topic of annual workshops associated with the main AI conferences, such as AAAI, IJCAI and ECAI.

Over the last two decades, qualitative reasoning (QR) has become an important domain in Artificial Intelligence. QDE (Qualitative Differential Equation) model learning (QML), as a branch of QR, has also received an increasing amount of attention; many systems have been proposed to solve various significant problems in this field. QML has been applied to a wide range of fields, including physics, biology and medical science. In this paper, we first identify the scope of this review by distinguishing QML from other QML systems, and then review all the noteworthy QML systems within this scope. The applications of QML in several application domains are also introduced briefly. Finally, the future directions of QML are explored from different perspectives.

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.

In this paper, we model knowledge dynamics in agents’ belief bases in a collaborative multi-agent system (MAS). Four change operators are introduced: expansion, contraction, prioritized revision, and non-prioritized revision. For all of them, both constructive definitions and an axiomatic characterization by representation theorems are given. We formally justify minimal change, consistency maintenance, and non-prioritization principles. These operators are based on an epistemic model for multi-source belief revision in which a rational way to weigh the beliefs using a credibility order among agents is developed. The defined operators can be seen as skills added to the agents improving the collective reasoning of a MAS.

Numerous argumentation systems have been proposed in the literature. Yet there often appears to be a shortfall between proposed systems and possible applications. In other words, there seems to be a need for further development of proposals for argumentation systems before they can be used widely in decision-support or knowledge management. I believe that this shortfall can be bridged by taking a hybrid approach. Whilst formal foundations are vital, systems that incorporate some of the practical ideas found in some of the informal approaches may make the resulting hybrid systems more useful. In informal approaches, there is often an emphasis on using graphical notation with symbols that relate more closely to the real-world concepts to be modelled. There may also be the incorporation of an argument ontology oriented to the user domain. Furthermore, in informal approaches there can be greater consideration of how users interact with the models, such as allowing users to edit arguments and to weight influences on graphs representing arguments. In this paper, I discuss some of the features of argumentation, review some key formal argumentation systems, identify some of the strengths and weaknesses of these formal proposals and finally consider some ways to develop formal proposals to give hybrid argumentation systems. To focus my discussions, I will consider some applications, in particular an application in analysing structured news reports.

Autonomous robots are the intelligent agents par excellence. We frequently define a robot as a machine that senses, thinks and acts, i.e., an agent. They are distinguished from software agents in that robots are embodied agents, situated in the real world. As such, they are subject both to the joys and sorrows of the world. They can be touched and seen and heard (sometimes even smelled!), they have physical dimensions, and they can exert force on other objects. These objects can be like a ball in the RoboCup or Mirosot robot soccer games, they can be parts to be assembled, airplanes to be washed, carpets to be vacuumed, terrain to be traversed or cameras to be aimed. On the other hand, since robots are agents in the world they are also subject to its physical laws, they have mass and inertia, their moving parts encounter friction and hence heat, no two parts are precisely alike, measurements are corrupted by noise, and alas, parts break. Of course, robots also contain computers, and hence they are also subject to the slings and arrows of computer misfortunes, both in hardware and software. Finally, the world into which we place these robots keeps changing, it is non-stationary and unstructured, so that we cannot predict its features accurately in advance.

Self-managing or autonomic computing is a highly emerging field having been championed by industry and academia alike. Essentially the management of the system is being handed to the system itself, therefore the issue of trust in terms of the decisions the system makes regarding itself, is of paramount interest. This paper considers four aspects of trust in self-adaptive computing systems with the aim to identify trends and pinpoint areas that require more investigation.

This paper presents several case studies which illustrate how constraint programming can benefit from the combination of global and local search techniques, offering a flexible and efficient platform for the design of combinatorial optimisation applications. For job-shop scheduling, we relate experiments with local search procedures that use global search to intensively explore a given neighbourhood, in the spirit of “shuffle” methods. For preemptive job-shop scheduling, two basic search strategies, Depth-First Search and Limited Discrepancy Search, are compared. For Vehicle Routing we report an Incremental Local Optimisation heuristic, combined with Limited Discrepancy Search. Finally, we show how ad hoc algebras can considerably enhance the design of heuristics based on local and global search within a constraint-programming environment. Experiments on vehicle routing will enlighten how such a language for “search and insert” control can enable automated tuning and discovery of new strategies adapted to the instances typology of the problem at stake.

Information imprecision and uncertainty exist in many real world applications, and such information would be retrieved, processed, shared, reused, and aligned in the maximum automatic way possible. As a popular family of formally well-founded and decidable knowledge representation languages, fuzzy Description Logics (fuzzy DLs), which extend DLs with fuzzy logic, are very well suited to cover for representing and reasoning with imprecision and uncertainty. Thus, a requirement naturally arises in many practical applications of knowledge-based systems, in particular the Semantic Web, because DLs are the logical foundation of the Semantic Web. Currently, there have been lots of fuzzy extensions of DLs with Zadeh's fuzzy logic theory papers published, to investigate fuzzy DLs and more importantly serve as identifying the direction of fuzzy DLs study. In this paper, we aim at providing a comprehensive literature overview of fuzzy DLs, and we focus our attention on fuzzy extensions of DLs based on fuzzy set theory. Other relevant formalisms that are based on approaches like probabilistic theory or non-monotonic logics are covered elsewhere. In detail, we first introduce the existing fuzzy DLs (including the syntax, semantics, knowledge base, and reasoning algorithm) from the origin, development (from weaker to stronger in expressive power), some special techniques, and so on. Then, the other important issues on fuzzy DLs, such as reasoning, querying, applications, and directions for future research, are also discussed in detail. Also, we make a comparison and analysis.