Agent systems based on the Belief, Desire and Intention model of Rao and Georgeff have been used for a number of successful applications. However, it is often difficult to learn how to apply such systems, due to the complexity of both the semantics of the system and the computational model. In addition, there is a gap between the semantics and the concepts that are presented to the programmer. In this paper we address these issues by re-casting the foundations of such systems into a logic programming framework. In particular we show how the integration of backward- and forward-chaining techniques for linear logic provides a natural starting point for this investigation. We discuss how the integrated system provides for the interaction between the proactive and reactive parts of the system, and we discuss several aspects of this interaction. In particular, one perhaps surprising outcome is that goals and plans may be thought of as declarative and procedural aspects of the same concept. We also discuss the language design issues for such a system, and particularly the way in which the potential choices for rule evaluation in a forward-chaining manner is crucial to the behaviour of the system.

Abstract. Agent systems based on the Belief, Desire and Intention model of Rao and Georgeff have been used for a number of successful applications. However, it is often difficult to learn how to apply such systems, due to the complexity of both the semantics of the system and the computational model. In addition, there is a gap between the semantics and the concepts that are presented to the programmer. One way to bridge this gap is to re-cast the foundations of such systems into a logic programming framework. In particular, the integration of backward- and forward-chaining techniques for linear logic provides a natural starting point for this investigation. In this paper we discuss the language design issues for such a system, and particularly the way in which the potential choices for rule evaluation in a forward-chaining manner is crucial to the behaviour of the system. 1 Introduction An increasingly popular programming paradigm is that of agent-oriented pro-gramming. This paradigm, often described as a natural successor to objectoriented programming [18], is highly suited for applications which are embeddedin complex dynamic environments, and is based on human concepts, such as beliefs, goals and plans. This allows a natural specification of sophisticated soft-ware systems in terms that are similar to human understanding, thus permitting programmers to concentrate on the critical properties of the application ratherthan getting absorbed in the intricate detail of a complicated environment. Agent technology has been used in areas for applications such as air traffic control, au-tomated manufacturing, and maintenance tasks on the space shuttle [19].