A probabilistic network is a graphical model that encodes probabilistic relationships between variables of interest. Such a model records qualitative influences between variables in addition to the numerical parameters of the probability distribution. As such it provides an ideal form for combining prior knowledge, which might be limited solely to experience of the influences between some of the variables of interest, and data. In this paper, we first show how data can be used to revise initial estimates of the parameters of a model. We then progress to showing how the structure of the model can be revised as data is obtained. Techniques for learning with incomplete data are also covered. In order to make the paper as self contained as possible, we start with an introduction to probability theory and probabilistic graphical models. The paper concludes with a short discussion on how these techniques can be applied to the problem of learning causal relationships between variables in a domain of interest.

In recent years, interest in intelligent agents has ballooned. With the WWW as a source of seemingly limitless opportunity and robotics platforms becoming cheaper and more easily maintained, the applications are myriad, challenging and within reach of even small groups.

Interest in the nature, development and use of ontologies is becoming increasingly widespread. Since the early nineties, numerous workshops have been held. Representatives from historically separate disciplines concerned with philosophical issues, knowledge acquisition and representation, planning, process management, database schema integration, natural language processing and enterprise modelling, came together to identify a common core of issues of interest. There was highly varied and inconsistent usage of a wide variety of terms, most notably, “ontology”, rendering cross-discipline communication difficult. However, progress was made toward understanding the commonality among the disciplines. Subsequent workshops addressed various aspects of the field, including theoretical issues, methodologies for building ontologies, as well as specific applications in government and industry.

Default reasoning is concerned with making inferences in cases where the information at hand is incomplete. In such cases it is necessary to make plausible assumptions, which in default reasoning are based on default rules. This paper gives an introduction to the field. It discusses in depth one particular approach, default logic, including properties, semantics and computational models. It also gives an overview of other ideas and approaches.

Traditional approaches to plan representation have focused on the generation of a sequence of actions and orderings. Knowledge rich models, which incorporate plan rationale, provide benefits to the planning process in a number of ways. The use of rationale in planning is reviewed in terms of causality, dependencies, and decisions. Each dimension addresses practical issues in the planning process, and adds value to the resultant plan. The contribution of this paper is to explore this categorisation, and to motivate the need to explicitly record and represent rationale knowledge for situated, mixed-initiative planning systems.

The objective of this paper is twofold. In its first part, we survey the state of the art in research on agent architectures. The architecture of an agent describes its modules and capabilities, and how these operate together. We structure the field by investigating three important research threads, i.e. architectures for reactive agents, deliberative agents and interacting agents. Then we describe various hybrid approaches that reconcile these three threads, aiming at a combination of different features like reactivity, deliberation and the ability to interact with other agents. These approaches are contrasted with architectural issues of recent agent-based work, including software agents, softbots, believable agents, as well as commercial agent-based systems. The second part of the paper addresses software engineers and system designers who are interested in applying agent technology to their problem domains. The objective of this part is to assist these readers in deciding which agent architecture to choose for a specific application. We characterise the most important domains to which the different approaches described in the first part have been applied, propose an application-related taxonomy of agents, and give a set of guidelines to select the right agent (architecture) for a given application.

Software agents have come a long way in the last ten or so years. This paper looks at the practical challenges many software agent developers face including: interfacing to legacy systems; developing distributed systems; agent competence; and trust and privacy issues. Because it covers such a broad area, it will focus on how the problems and issues have changed over the years, and highlight some of the perennial problems.

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.

In recent years, the capabilities of knowledge-based systems to communicate with their users have evolved from simple interactions to complex dialogues. With this evolution comes a need to understand what makes a good dialogue. In this paper, we are concerned with dialogue coherence. We review the notion of focus, which partly explains this property, and its use for user-system communication. First, we examine the major theories dealing with this notion. We describe what their contribution is and how they differ. Then, we illustrate the benefits of using the notion of focus and especially the improvement in text coherence. We pay particular attention to how the notion can concretely be implemented. Its integration with other techniques and theories is described. We conclude the paper by pointing out remaining issues in the understanding of the notion of focus. The contribution of this paper is to provide a classification of the theories of focus and to show the improvements they offer in elaborate user-system dialogues.

Information retrieval is the science concerned with the efficient and effective storage of information for the later retrieval and use by interested parties. During the last forty years, a plethora of information retrieval models and their variations have emerged. Logic-based models were launched to provide a rich and uniform representation of information and its semantics with the aim to improve information retrieval effectiveness. This approach was first advanced in 1986 by Van Rijsbergen with the so-called logical uncertainty principle. Since then, various logic-based models have been developed. This paper presents an introduction to and a survey of the use of logic for information retrieval modelling.

This is a comprehensive description of the Enterprise Ontology, a collection of terms and definitions relevant to business enterprises. We state its intended purposes, describe how we went about building it, define all the terms and describe our experiences in converting these into formal definitions. We then describe how we used the Enterprise Ontology and give an evaluation which compares the actual uses with original purposes. We conclude by summarising what we have learned. The Enterprise Ontology was developed within the Enterprise Project, a collaborative effort to provide a framework for enterprise modelling. The ontology was built to serve as a basis for this framework which includes methods and a computer tool set for enterprise modelling. We give an overview of the Enterprise Project, elaborate on the intended use of the ontology, and give a brief overview of the process we went through to build it. The scope of the Enterprise Ontology covers those core concepts required for the project, which will appeal to a wider audience. We present natural language definitions for all the terms, starting with the foundational concepts (e.g. entity, relationship, actor). These are used to define the main body of terms, which are divided into the following subject areas: activities, organisation, strategy and marketing. We review some of the things learned during the formalisation process of converting the natural language definitions into Ontolingua. We identify and propose solutions for what may be general problems occurring in the development of a wide range of ontologies in other domains. We then characterise in general terms the sorts of issues that will be faced when converting an informal ontology into a formal one. Finally, we describe our experiences in using the Enterprise Ontology. We compare these with the intended uses, noting our successes and failures. We conclude with an overall evaluation and summary of what we have learned.

The First International Conference on Autonomous Agents brought together researchers concerned with implementing systems that perceive and act in dynamic, unpredictable environments, that coordinate interoperation among complementary component capabilities, and that perform significant jobs with a high degree of autonomy.

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.

Interest in agent-oriented technology continues to grow, both at a theoretical level and a practical level, with the UK maintaining a strong representation in the area in both academic institutions and commercial organizations. In December 1997, the Second UK Workshop on Foundations of Multi-Agent Systems (FoMAS'97), held at the University of Warwick, built on the success of FoMAS'96 a year earlier (Luck, 1997; Doran et al., 1997; d'Inverno et al., 1997; Fisher et al., 1997) in seeking to provide a forum for academics and industrialists within the UK to present and discuss current directions in research and applications development. Supported by the EPSRC and HP Labs, FoMAS'97 was expanded to two days with invited presentations from Professor Ken Binmore of UCL, and Professor Cristiano Castelfranchi of the Italian National Research Council's Institute of Psychology, in addition to paper presentations and panel discussions. The aim again was to provide an opportunity for promoting and supporting activity in the research and development of multi-agent systems.

This document provides the specification of the Process Interchange Format (PIF) version 1.2. The goal of this work is to develop an interchange format to help automatically exchange process descriptions among a wide variety of business process modelling and support systems such as workflow software, flow charting tools, planners, process simulation systems and process repositories. Instead of having to write ad hoc translators for each pair of such systems each system will only need to have a single translator for converting process descriptions in that system into and out of the common PIF format. Then any system will be able to automatically exchange basic process descriptions with any other system. This document describes the PIF-CORE 1.2, i.e. the core set of object types (such as activities, agents and prerequisite relations) that can be used to describe the basic elements of any process. The document also describes a framework for extending the core set of object types to include additional information needed in specific applications. These extended descriptions are exchanged in such a way that the common elements are interpretable by any PIF translator, and the additional elements are interpretable by any translator that knows about the extensions. The PIF format was developed by a working group including representatives from several universities and companies, and has been used for experimental automatic translations among systems developed independently at three of these sites. This document is being distributed in the hopes that other groups will comment upon the interchange format proposed here, and that this format (or future versions of it) may be useful to other groups as well. The PIF Document 1.0 was released in December 1994, and the current document reports the revised PIF that incorporate the feedback received since then.

As the number of deployed multi-agent applications increases, further and better experience with the technology is gained, enabling a strong evaluation of the field from a more practical perspective. In particular, questions relating to how the theory of multi-agent systems impacts on practice, and how the practical development itself compares with other technologies, can be answered in the light of a heightened level of maturity. Given the tensions between theoreticians and practitioners in computing in general, let alone their spats in AI or multi-agent systems in particular, the discussion on agent systems and applications was both vigorous and enthusiastic

The Defense Advanced Research Projects Agency (DARPA) and US Air Force Research Laboratory Planning Initiative (ARPI) has initiated a project to draw on the range of previous work in planning and activity ontologies to create a practically useful Shared Planning and Activity Representation (SPAR) for use in technology and applications projects within their communities. This article describes the previous work which has been used to create the initial SPAR representation. Key examples of the work drawn upon are published in this issue. The paper provides a comprehensive bibliography and related world wide web resources for work in the area of plan, process and activity representation. SPAR is now being subjected to refinement during several review cycles by a number of expert and user panels.

Software agents help automate a variety of tasks including those involved in buying and selling products over the Internet. This paper surveys several of these agent-mediated electronic commerce systems by describing their roles in the context of a Consumer Buying Behavior (CBB) model. The CBB model we present augments traditional marketing models with concepts from Software Agents research to accommodate electronic markets. We then discuss the variety of Artificial Intelligence techniques that support agent mediation and conclude with future directions of agent-mediated electronic commerce research.