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.

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).

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.