This is the second of two special issues focusing on the integration of artificial intelligence (AI) and operations research (OR) techniques for solving hard computational problems, with an emphasis on planning and scheduling. Both the AI and the OR community have developed sophisticated techniques to tackle such challenging problems. OR has relied heavily on mathematical programming formulations such as integer and linear programming, while AI has developed constraint-based search techniques and inference methods. Recently, we have seen a convergence of ideas, drawing on the individual strengths of these paradigms.

Autonomous agents are systems capable of autonomous decision-making in real-time environments. Computation is a valuable resource for such decision-making, and yet the amount of computation that an autonomous agent may carry out will be limited. It follows that an agent must be equipped with a mechanism that enables it to make the best possible use of the computational resources at its disposal. In this paper we review three approaches to the control of computation in resource-bounded agents. In addition to a detailed description of each framework, this paper compares and contrasts the approaches, and lists the advantages and disadvantages of each.

This article gives a comprehensive overview of techniques for personalised hypermedia presentation. It describes the data about the computer user, the computer usage and the physical environment that can be taken into account when adapting hypermedia pages to the needs of the current user. Methods for acquiring these data, for representing them as models in formal systems and for making generalisations and predictions about the user based thereon are discussed. Different types of hypermedia adaptation to the individual user's needs are distinguished and recommendations for further research and applications given. While the focus of the article is on hypermedia adaptation for improving customer relationship management utilising the World Wide Web, many of the techniques and distinctions also apply to other types of personalised hypermedia applications within and outside the World Wide Web, like adaptive educational systems.

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.

Many solutions to AI problems require the task to be represented in one of a multitude of rigorous mathematical formalisms. The construction of such mathematical models forms a difficult problem which is often left to the user of the problem-solver. This void between problem-solvers and their problems is studied by the eclectic field of automated modelling. Within this field, compositional modelling, a knowledge-based methodology for system-modelling, has established itself as a leading approach. In general, a compositional modeller organises knowledge in a structure of composable fragments that relate to particular system components or processes. Its embedded inference mechanism chooses the appropriate fragments with respect to a given problem, instantiates and assembles them into a consistent system model. Many different types of compositional modeller exist, however, with significant differences in their knowledge representation and approach to inference. This paper examines compositional modelling. It presents a general framework for building and analysing compositional modellers. Based on this framework, a number of influential compositional modellers are examined and compared. The paper also identifies the strengths and weaknesses of compositional modelling and discusses some typical applications.

Maths programming (MP) and constraint programming (CP) are two techniques that are able to solve difficult industrial optimisation problems. The purpose of this paper is to compare them from an algorithmic and a modelling point of view. Algorithmic principles of each approach are described and contrasted. Some ways of combining both techniques are also introduced.

Argumentation concepts have been applied to numerous knowledge engineering endeavours in recent years. For example, a variety of logics have been developed to represent argumentation in the context of a dialectical situation such as a dialogue. In contrast to the dialectical approach, argumentation has also been used to structure knowledge. This can be seen as a non-dialectical approach. The Toulmin argument structure has often been used to structure knowledge non-dialectically yet most studies that apply the Toulmin structure do not use the original structure but vary one or more components. Variations to the Toulmin structure can be understood as different ways to integrate a dialectical perspective with a non-dialectical one. Drawing the dialectical/non-dialectical distinction enables the specification of a framework called the generic actual argument model that is expressly non-dialectical. The framework enables the development of knowledge-based systems that integrate a variety of inference procedures, combine information retrieval with reasoning and facilitate automated document drafting. Furthermore, the non-dialectical framework provides the foundation for simple dialectical models. Systems based on our approach have been developed in family law, refugee law, determining eligibility for government legal aid, copyright law and e-tourism.

This paper focuses upon the contribution which adequate use of the latest development in IT can make to the enhancement, development and improvement of professional expertise in the construction domain. The paper is based on the author's personal expertise and involvement in several UK- and European-funded research projects. First, the paper gives an overview of the benefits that can be gained from improved information and knowledge management in the construction industry. The latest developments in information and communication technologies are then described and presented as an enabler for effective knowledge management. Finally, the paper reviews the most promising techniques for information and knowledge management in the construction domain, highlighting the advantages and disadvantages of each of them, as well as future directions for knowledge management in construction.

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.

Agent-Oriented Software Engineering (AOSE) is rapidly emerging in response to urgent needs in both software engineering and agent-based computing. While these two disciplines coexisted without remarkable interaction until some years ago, today there is rich and fruitful interaction among them and various approaches are available that bring together techniques, concepts and ideas from both sides. This article offers a guide to the broad body of literature on AOSE. The guide, which is intended to be of value to both researchers and practitioners, is structured according to key issues and key topics that arise when dealing with AOSE: methods and frameworks for requirements engineering, analysis, design, and implementation; languages for programming, communication and coordination and ontology specification; and development tools and platforms.

The first International Workshop on Chance Discovery (CD) was held in the hot-springs resort of Matsue, Shimani Prefecture, Japan, on 22 May 2001, as part of the Fifteenth Annual Conference of the Japanese Society for Artificial Intelligence (JSAI-2001). Thirteen presentations were made to the workshop (Ohsawa, 2001), with 25 people attending. The majority of presenters and attendees were from Japan. An edited selection of the papers presented will be included in a volume of papers from the various International Workshops of JSAI-2001, to be published in the Springer series, Advanced Information Processing. A forthcoming special issue of the journal New Generation Computing will also address this topic.

Many optimisation problems involve combinatorial constraints on continuous variables. An example of a combinatorial constraint is that at most one variable in a group of nonnegative variables may be positive. Traditionally, in the mathematical programming community, such problems have been modeled as mixed-integer programs by introducing auxiliary binary variables and additional constraints. Because the number of variables and constraints becomes larger and the combinatorial structure is not used to advantage, these mixed-integer programming models may not be solved satisfactorily, except for small instances. Traditionally, constraint programming approaches to such problems keep and use the combinatorial structure, but do not use linear programming bounds in the search for an optimal solution. Here we present a branch-and-cut approach that considers the combinatorial constraints without the introduction of binary variables. We review the development of this approach and show how strong constraints can be derived using ideas from polyhedral combinatorics. To illustrate the ideas, we present a production scheduling model that arises in the manufacture of fibre optic cables.

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.

There is little doubt that the strength and breadth of UK research into multi-agent systems continues to grow as we move into the new millennium. In the middle of an extremely cold December in 2000, the Third UK Workshop on Multi-Agent Systems (UKMAS 2001) was held at St Catherine's College, Oxford. This was the fifth such meeting in as many years, generously sponsored by EPSRC, FIPA (The Foundation for Intelligent Physical Agents) and Hewlett Packard.

The concept of holonic systems has its roots in the desire to understand the structure of natural systems (e.g. living organisms and social organisations) and in particular their ability to behave in a stable yet flexible manner in the face of change. It is not surprising that the lessons learned from these natural systems could help with the design and control of complex man-made systems. However, a key issue is, how can one translate holonic concepts to real industrial environments? For example, one of the key holonic concepts, the holon, can be described as a self-contained autonomous and cooperative entity; when deciding how to implement holons, software agents appear to be the logical choice. In this paper, we summarise the presentations and discussions from a workshop held at the recent International Conference on Autonomous Agents that focused on this issue and brought together researchers from both the holonic systems and the multi-agents systems communities.

In this paper we describe the framework we have developed in KIDS (Kestrel Interactive Development System) for generating efficient constraint satisfaction programs. We have used KIDS to synthesise global search scheduling programs that have proved to be dramatically faster than other programs running the same data. We focus on the underlying ideas that lead to this efficiency. The key to the efficiency is the reduction of the size of the search space by an effective representation of sets of possible solutions (solution spaces) that allows efficient constraint propagation and pruning at the level of solution spaces. Moving to a solution space representation involves a problem reformulation. Having found a solution to the reformulated problem, an extraction phase extracts solutions to the original problem. We show how constraints from the original problem can be automatically reformulated and specialised in order to derive efficient propagation code automatically. Our solution methods exploit the semi-lattice structure of our solution spaces.

In recent years, multi-agent systems (MASs) have received increasing attention in the artificial intelligence community. Research in multi-agent systems involves the investigation of autonomous, rational and flexible behaviour of entities such as software programs or robots, and their interaction and coordination in such diverse areas as robotics (Kitano et al., 1997), information retrieval and management (Klusch, 1999), and simulation (Gilbert & Conte, 1995). When designing agent systems, it is impossible to foresee all the potential situations an agent may encounter and specify an agent behaviour optimally in advance. Agents therefore have to learn from, and adapt to, their environment, especially in a multi-agent setting.

The use of agents in electronic commerce has been explored greatly over the past several years. A large majority of this effort is toward commerce where businesses have direct transactions with consumers (B2C). However, the transactions that occur between businesses (B2B) are far more prevalent than B2C. Research where agents are used for B2B can be classified in five basic areas, service discovery, mediation, negotiation, process management (be it workflow or supply-chain management), and evaluation. At the 2001 International Bi-Conference Sessions on Agent-Based Approaches to B2B Interoperability (AgentB2B), practitioners were invited to present their research and industry efforts in each of these areas. This paper summaries the work and conclusions presented at these two events.