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.

Emphasizing theory and implementation issues more than specific applications and Prolog programming techniques, this book by David Maier and Davis S. Warren, respected researchers in logic programming, is a superb book.

He is a and Information Science at The and Information Science at The Distinguished Professor of Computer Ohio State University, Columbus, Ohio State University, Columbus, Science at the University of Minnesota, Ohio 43210. He is currently writing Ohio 43210. He is currently writing a position he has held since 1984.

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.

Empirical evidence suggests that searching deeper in game trees using the minimax propagation rule usually improves the quality of decisions significantly. However, despite many recent theoretical analyses of the effects of minimax look ahead, however, this phenomenon has still not been convincingly explained. Instead, much attention has been given to so-called pathological behavior, which occurs under certain assumptions. This article supports the view that pathology is a direct result of these underlying theoretical assumptions. Pathology does not occur in practice, because these assumptions do not apply in realistic domains. The article presents several arguments in favor of minimaxing and focuses attention on the gap between their analytical formulation and their practical meaning. A new model is presented based on the strict separation of static and dynamic aspects in practical programs. finally, certain methods of improving minimax look-ahead are discussed, drawing on insights gained from this research.

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. This article attempts to refine that goal and suggest how to get there. 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. I focus on qualitative physics as a critical area where progress is needed, both in terms of representations and styles of reasoning.