WINTER 1988 79 Notes to Financial Statements Program Committee (reported by Reid accounting principles applied on a Smith, Program Co-Chair).

Evaluation should be a mechanism of progress both within and across AI research projects. For the individual, evaluation can tell us how and why our methods and programs work and, so, tell us how our research should proceed. For the community, evaluation expedites the understanding of available methods and, so, their integration into further research. In this article, we present a five-stage model of AI research and describe guidelines for evaluation that are appropriate for each stage. These guidelines, in the form of evaluation criteria and techniques, suggest how to perform evaluation. We conclude with a set of recommendations that suggest how to encourage the evaluation of AI research.

The Fifth Annual Theoretical Issues in Conceptual Information Processing Workshop took place in Washington, D.C. in June 1987. About 100 participants gathered to hear several invited talks and panels discussing the issues relating to artificial intelligence and cognitive science.

AAAI is a society devoted to supporting the progress in science, technology and applications of AI. I thought I would use this occasion to share with you some of my thoughts on the recent advances in AI, the insights and theoretical foundations that have emerged out of the past thirty years of stable, sustained, systematic explorations in our field, and the grand challenges motivating the research in our field.

Second, the problem domain of the used be as good as possible. Two The application task requires little task is stable. This means that the characteristics of the domain expert or no common sense. Although domain should be well established can help determine the degree of researchers are continuing to study and unlikely to undergo vast changes expertise. First, the expert is highly the representation of commonsense during the life of the expert system respected by experienced people in the knowledge, no practical systems have project. This stability does not require domain field. Because the goal of the been developed to date (Lenat, that the problem-solving process project is often to simulate the Prakash, and Shepherd 1986). A problem required to perform the task be well expert's performance, this expert requiring common sense on the understood, simply that the basics of should be viewed by others as a genuine part of the expert should be left to a the task domain be established.

The following is a synopsis of the findings of the first AAAI Workshop on AI Applications to Battle Management held at the University of Washington, 16 July 1987. This paper served as a focus for the workshop presentations and discussions and was augmented by the workshop presentations; it can also serve as a roadmap of topics for future workshops. AI can provide battle management with such capabilities as sensor data fusion and adaptive simulations. Also, several key needs in battle management will be AI research topics for years to come, such as understanding free text and inferencing in real time.

The PROSENET/TEXTNET approach is designed to facilitate the generation of polished prose by an expert system. The approach uses the augmented transition network (ATN) formalism to help structure prose generation at the phrase, sentence, and paragraph levels. The approach also uses expressive frames to help give the expert system builder considerable freedom to organize material flexibly at the paragraph level. The PROSENET /TEXTNET approach has been used in a number of prototype expert systems in medical domains, and has proved to be a convenient and powerful tool.

The goal of intelligent computer-aided engineering (ICAE) is to construct computer programs that capture a significant fraction of an engineer's knowledge. Today, ICAE systems are a goal, not a reality. We begin by examining several scenarios of what ICAE systems could be like. Next we describe why ICAE won't evolve directly from current applications of expert system technology to engineering problems.

Within the academic and professional auditing communities, there has been growing concern about how to accurately assess the various risks associated with performing an audit. These risks are difficult to conceptualize in terms of numeric estimates.