The purpose of this paper is to review the use of knowledge-based systems and artificial intelligence (AI) in business. Part I of this paper examines domain applications, the use of different knowledge representations, unique contributions for knowledge acquisition from the development of systems for business applications, and models of explanation for systems developed for business. In addition, the paper discusses the use of both normative and descriptive AI systems to model business judgments. Finally, the paper provides a summary of publication sources and meetings on business systems.

This special issue of The Knowledge Engineering Review includes three papers on the subject of automated knowledge-acquisition tools. Gaines and Shaw (1993) review knowledge acquisition tools based on personal-construct psychology; Birmingham and Klinker (1993) review tools based on predefined models of problem-solving methods; and Eriksson and Musen (1993) review metalevel tools that developers may use to build other knowledge acquisition tools. Although this trio of papers by no means summarizes all recent developments in computer-based assistance for knowledge engineering, these contributions provide examples of three important research frontiers that, in many ways, trace their origins to early expert systems research.

Reasoning about actions and plans is a vital aspect of the rational behaviour of intelligent agents, and hence represents a major research domain in artificial intelligence. Much work has been undertaken to develop logic-based formalisms and problem solving procedures for plan representation and plan synthesis. This paper consists of a survey of various paradigms for reasoning about actions and plans in artificial intelligence. Attention is focused on the logic-based theoretical frameworks which have built a formal foundation for the domain-independent approaches to the general principles of reasoning about actions and plans.

Expert critiquing systems present an interesting and important complement to conventional expert systems. The basic idea is to emphasize the support for the user's own decision making rather than to let the system independently suggest a solution to a given problem. Thus it is assumed that the user initially proposes a decision or course of action. The system then reviews this suggestion relative to known circumstance, tries to evaluate the proposed solution, provides suggestions for improvements, draws attention to possible risks, indicates alternatives, and evaluates their merits, etc. A critiquing system is characterized both by its approach to problem solving and by its style of interaction with the user, as will be detailed below.

In the past decade, expert systems have been applied to a wide variety of application tasks. A central problem of expert system development and maintenance is the demand placed on knowledge engineers and domain experts. A commonly proposed solution is knowledge-acquisition tools. This paper reviews a class of knowledge-acquisition tools that presuppose the problem-solving method, as well as the structure of the knowledge base. These explicit problem-solving models are exploited by the tools during knowledge-acquisition, knowledge generalization, error checking and code generation.

Human understanding in design evolves through a process of critiquing existing knowledge and consequently expanding the store of design knowledge. Critiquing is a dialogue in which the interjection of a reasoned opinion about a product or action triggers further reflection on or changes to the artifact being designed. Our work has focused on applying this successful human critiquing paradigm to human-computer interaction. We argue that computer-based critiquing systems are most effective when they are embedded in domain-oriented design environments, which are knowledge-based computer systems that support designers in specifying a problem and constructing a solution. Embedded critics play a number of important roles in such design environments: (1) they increase the designer's understanding of design situations by pointing out problematic situations early in the design process; (2) they support the integration of problem framing and problem solving by providing a linkage between the design specification and the design construction; and (3) they help designers access relevant information in the large information spaces provided by the design environment. Three embedded critiquing mechanisms—generic, specific, and interpretive critics—are presented, and their complementary roles within the design environment architecture are described.

In this paper, we present two broad styles of KBS reasoner: those based primarily on some general, explicit model of the knowledge of the domain (whether that model be expressed by heuristic rules or by a deep model of structure and function), which we term domain model-based reasoners; and those based primarily on a set of examples of events in the domain, which we term example-based reasoners (EBR), of which case-based reasoners are a subset. The aim of this paper is to guide developers in considering the trade-offs between these different styles of reasoning. We believe that this cannot be done in general, but may be possible for specific domains. Thus, the paper provides an example analysis of the usefulness of these reasoning styles. We assess the suitability of these styles against a series of requirements which we have identified that KBSs must fulfil if they are to support help desk operations. We conclude that EBR systems are more likely to meet those requirements (the analysis draws on our earlier work in Bridge & Dearden, 1992).

Database researchers have recognized that integrating a production rules facility into a database system provides a uniform mechanism for a number of advanced database features including integrity constraint enforcement, derived data maintenance, triggers, protection, version control, and others. In addition, a database system with rule processing capabilities provides a useful platform for large and efficient knowledge-base and expert systems. Database systems with production rules are referred to as active database systems, and the field of active database systems has indeed been active. This paper summarizes current work in active database systems, and suggests future research directions. Topics covered include database rule languages, rule processing semantics, and implementation issues.

Interactive knowledge-acquisition (KA) programs allow users to enter relevant domain knowledge according to a model predefined by the tool developers. KA tools are designed to provide conceptual models of the knowledge to their users. Many different classes of models are possible, resulting in different categories of tools. Whenever it is possible to describe KA tools according to explicit conceptual models, it is also possible to edit the models and to instantiate new KA tools automatically for specialized purposes. Several meta-tools that address this task have been implemented. Meta-tools provide developers of domain-specific KA tools with generic design models, or meta-views, of the emerging KA tools. The same KA tool can be specified according to several alternative meta-views.

Distributed Artificial Intelligence systems, in which multiple agents interact to improve their individual performance and to enhance the systems' overall utility, are becoming an increasingly pervasive means of conceptualising a diverse range of applications. As the discipline matures, researchers are beginning to strive for the underlying theories and principles which guide the central processes of coordination and cooperation. Here agent communities are modelled using a distributed goal search formalism, and it is argued that commitments (pledges to undertake a specific course of action) and conventions (means of monitoring commitments in changing circumstances) are the foundation of coordination in multi-agent systems. An analysis of existing coordination models which use concepts akin to commitments and conventions is undertaken before a new unifying framework is presented. Finally, a number of prominent coordination techniques which do not explicitly involve commitments or conventions are reformulated in these terms to demonstrate their compliance with the central hypothesis of this paper.

Knowledge collection systems often assume they are cooperating with an unbiased expert. They have few functions for checking and fixing the realism of the expertise transferred to the knowledge base, plan, document or other product of the interaction. The same problem arises when human knowledge engineers interview experts. The knowledge engineer may suffer from the same biases as the domain expert. Such biases remain in the knowledge base and cause difficulties for years to come.

To prevent such difficulties, this paper introduces the reader to “critic engineering”, a methodology that is useful when it is necessary to doubt, trap and repair expert judgment during a knowledge collection process. With the use of this method, the human expert and knowledge-based critic form a cooperative system. Neither agent alone can complete the task as well as the two together.

The methodology suggested here offers a number of extensions to traditional knowledge engineering techniques. Traditional knowledge engineering often answers the questions delineated in generic task (GT) theory, yet GT theory fails to provide four additional sets of questions that one must answer to engineer a knowledge base, plan, design or diagnosis when the expert is prone to error. This extended methodology is called “critic engineering”.

Active rules have been a standard technique in artificial intelligence (AI) for almost two decades. Variants of the Al methods are currently being adapted to provide database systems with the ability to respond reactively to events and database state changes. This paper gives an overview of developments in reactive processing database research, concentrating on active databases that are integrated with relational and object-oriented systems. A general presentation of “trigger”-based processing techniques is given, with a detailed review of active relational and object-oriented database models. An overview of technologies for active processing in AI is also presented, and some common and contrasting themes in database and AI technology identified.

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 central area of application for knowledge-based systems is for giving consultative advice to the user. Such systems engage the user in a dialogue in the process of collecting enough information to be able to infer a conclusion from the knowledge base. Traditionally, then, the main initiative in the consultation process has been allocated to the system