A funny thing happened to me at IJCAI-81. I went to a panel on "Education in AI" and stepped back into an argument that I had thought settled several years ago. The debate was between the "scruffies," led by Roger Schank and Ed Feignbaum, and the "neats," led by Nils Nilsson. The neats argued that no education in AI was complete without a strong theoretical component, containing, for instance, courses on predicate logic and automata theory. The scruffies maintained that such a theoretical component was not only unnecessary, but harmful.

This paper presents the view that artificial intelligence (AI) is primarily concerned with propositional languages for representing knowledge and with techniques for manipulating these representations. In this respect, AI is analogous to applied in a variety of other subject areas. Typically, AI research (or should be) more concerned with the general form and properties of representational languages and methods than it is with the context being described by these languages. Notable exceptions involve "commonsense" knowledge about the everyday would ( no other specialty claims this subject area as its own ), and metaknowledge (or knowledge about the properties itself). In these areas AI is concerned with content as well as form. We also observe that the technology that seems to underly peripheral sensory and motor activities (analogous to low-level animal or human vision and muscle control) seems to be quite different from the technology that seems to underly cognitive reasoning and problem solving. Some definitions of AI would include peripheral as well as cognitive processes; here we argue against including the peripheral processes.

The Nobel Prize in Economics was awarded to Herbert Simon in 1978. At Carnegie-Mellon University he holds the title of Professor of Computer Science and Psychology. These two facts together delineate the range and uniqueness of his contributions in creating meaningful interactions among fields that developed in isolation but that are all concerned with human decision-making and problem-solving processes. In particular, Simon has brought the insights of decision theory, organization theory (especially as it applies to the business firm), behavior modeling, cognitive psychology, and the study of artificial intelligence to bear on economic questions. This has led not only to new conceptual dimensions for theoretical constructions, but also to a new humanizing realism in economics, a way of taking into account and dealing with human behavior and interactions that lie at the root of all economic activity.

Practitioners of ethnography, in seeking to discover and describe complex patterns of behavior, face a number of serious problems.

Sussman (Sussman 1975) has explored how debugging a plan can lead to a problem solution. Most work on planning has been research done for its own sake.

PROLOG is a simple and powerful progamming language based on first-order logic. This paper examines two possible extensions to the language which would generally be considered "higher-order".t The first extension introduces lambda expressions and predicate variables so that functions and relations can be treated as 'first class' data objects. We argue that this extension does not add anything to the real power of the language. The other extension concerns the introduction of set expressions to denote the set of all (provable) solutions to some goal. We argue that this extension does indeed fill a real gap in the language, but must be defined with care.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.

A practical criterion for the success of a knowledge-based problem-solving system is its usefulness as a tool to those working in its specialized domain of expertise. This paper describes an evaluation and several applications of a knowledge-based system, the PROSPECTOR consultant for mineral exploration. PROSPECTOR is a rule-based judgmental reasoning system that evaluates the mineral potential of a site or region with respect to inference network models of specific classes of ore deposits. Knowledge about a particular type of ore deposit is encoded in a computational model representing observable geological features and the relative significance thereof.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.

The Yale Artificial Intelligence Project, under the direction of Professor Roger C. Schank, supports a number of research projects. Most of this research is in the02-02 area of attempting to model the processes involved in human understanding, with a current emphasis on memory models and the processes involved in learning.

Advanced Information and Decision Systems (AI-DS) is a relatively new, employee-owned company that does basic and applied research, product development, and consulting in the fields of artificial intelligence, computer science, decision analysis, operations research, control theory, estimation theory, and signal processing. AI&DS performs studies, analyses, systems design and evaluation, and software development for a variety of industrial clients and government agencies, including the Department of Defense and Energy.

This article is the second planned excerpt from the Handbook of Artificial Intelligence being complied at Stanford University. This overview of the Handbook chapter on search, like the overview of natural language research we printed in the first issue, introduces the important ideas and techniques, which are discussed in detail later in the chapter. Cross-references to other articles in the Handbook have been removed -- terms discussed in more detail elsewhere are italicized. The author would like to note that this article draws on material generously made available by Nils Nilsson for use in the Handbook.