This paper explores the implications of research results in behavioural decision theory on knowledge engineering. Behavioural decision theory, with its performance (versus process) orientation, can tell us a great deal about the validity of human expert knowledge, and when it should be modelled. A brief history of behavioural decision theory is provided. Implications for knowledge elicitation and representation are discussed. An approach to knowledge engineering is proposed that takes into account these implications.

Research on planning in AI can be separated into the two major areas: plan generation and plan representation. Most AI planners to date have been based on the STRIPS planning representation. This representation has a number of limitations. Much recent work in plan representation has addressed these limitations. It was shown that Decision Theory can be used to remove a number of the limitations. Furthermore, the decision theoretic framework provides a precise definition of rational behaviour. There remain open questions within decision theory regarding belief revision and causality. It should be noted that these problems are not artifacts of the representation. Rather they arise because the rich representation allows their formulation. Some work integrating AI and decision theoretic approaches to planning has been done but this remains a largely untouched research area.

We see two main avenues for fruitful research. First, the straightforward decision theoretic formulation of planning is computationally impractical. Techniques need to be developed to do efficient decision theoretic planning. Work in AI plan generation has exploited information contained the structure of qualitative representations to guide efficient plan construction. These techniques should be applied to decision theoretic representations as well. Second, AI has developed many representations that allow useful structuring of knowledge about the world. Decision Theory has concentrated on representing beliefs and desires. Integration of AI and decision theoretic representations would yield powerful representation languages. Some of the benefits of such work can already be seen in the research combining temporal and decision theoretic representations.

Frame systems occupy an important place among formalisms for computer-based knowledge representation. A common concern about frame systems, however, is that they are not efficient enough. We argue that this is not necessarily true of all possible systems, and that the trade-off between generality and efficiency has not been fully explored. While many systems provide generality at the expense of performance, systems closer to the low end of the spectrum have not been investigated nearly as much. Those systems are well suited for applications that need flexible knowledge representation but cannot afford the high performance price.

We describe in detail KR, a very efficient frame system that provides mechanisms for knowledge representation including user-defined inheritance and relations, object-oriented programming, and constraint maintenance. The system is simple and compact and does not include some of the more complex functionality, but it is highly optimized and offers excellent performance for a variety of applications.