Editors' Note: Many expert systems require some means criticisms of this approach from those steeped in the practical of handling heuristic rules whose conclusions are less than certain issues of constructing large rule-based expert systems. Abstract the expert system draws inferences in solving different problems. Doyle's paper argues that it is difficult for a human expert "certainty factors," and in spite of the experimentally observed insensitivity of system performance to perturbations of the chosen values Recent successes of "expert systems" stem from much Research Projects Agency (DOD), ARPA Order No. 3597, monitored In the following, we explain the modified approach together with its practical and theoretical attractions. The client's income bracket is 50%, can be found (Minsky, 1975; Shortliffe & Buchanan, 1975; and 2. The client carefully studies market trends, Duda, Hart, & Nilsson, 1976; Szolovits, 1978; Szolovits & THEN: 3. There is evidence (0.8) that the investment Pauker, 1978). Reasoned Assumptions (from Davis, 1979) and would use the rule to draw conclusions whose "certainty factors" depend on the observed certainty Although our approach usually approximates that of Bayesian probabilities, accommodates representational systems based on "frames" namely as subjective degrees of belief.

The primary goal of the Artificial Intelligence Laboratory is to understand how computers can be made to exhibit intelligence. Two corollary goals are to make computers more useful and to understand certain aspects of human intelligence. Current research includes work on computer robotics and vision, expert systems, learning and commonsense reasoning, natural language understanding, and computer architecture.

It may come as a surprise to some to be told that the modern digital computer is really quite old in concept, and the year 1984 will be celebrated as the 150th anniversary of the invention of the first computer the Analytical Engine of the Englishman Charles Babbage. One hundred and fifty years is really quite a long period of time in terms of modern science and industry and, at first glance, it seems unduly long for new concept to come into full fruition. Unfortunately, Charles Babbage was ahead of his time, and it took one hundred years of technical development, the impetus of the second World War and the perception of John Von Neumann to bring the computer into being. Now twenty years later and with several generations of computer behind us, we are in a position to make a somewhat more meaningful prognosis than appeared possible in, say 1948. We can only hope that we will not be as far off actuality as we believe George Orwell to be, or as far off in our time scale as were Charles Babbage and his almost equally famous interpreter, Lady Lovelace.

The basic thesis put forth in this article is that a problem solver is essentially an interpreter that carries out computations implicit in the problem formulation. A good problem formulation gives rise to what is conventionally called a strong problem solver; poor formulations correspond to weak problem solvers. Knowledge-based systems are discussed in the context of this thesis. We also make observations about the relationship between search strategy and problem formulation.

This article describes a theory of reasoning about uncertainly, based on a representation of states of certainly called endorsements. The theory of endorsements is an alternative to numerical methods for reasoning about uncertainly, such as subjective Bayesian methods (Shortliffe and Buchanan, 1975; Duda hart, and Nilsson, 1976) and Shafer-dempster theory (Shafer, 1976). The fundamental concern with numerical representations of certainty is that they hide the reasoning about uncertainty. While numbers are easy to propagate over inferences, what the numbers mean is unclear. The theory of endorsements provide a richer representation of the factors that affect certainty and supports multiple strategies for dealing with uncertainty.

Various groups of ascertainable individuals have been granted the status of "persons" under American law, while that status has been denied to other groups. This article examines various analogies that might be drawn by courts in deciding whether to extend "person" status to intelligent machines, and the limitations that might be placed upon such recognition. As an alternative analysis, this article questions the legal status of various human/machine interfaces, and notes the difficulty in establishing an absolute point beyond which legal recognition will not extend.

The third national conference promotes research in the field of AI by bringing together individuals from government, industry, and academia and by providing a published record of the conference as proceedings.

Our group's work in medical decision making has led us to formulate a framework for expert system design, in particular about how the domain knowledge may be decomposed into substructures. We propose that there exist different problem-solving types, i.e., uses of knowledge, and corresponding to each is a separate substructure specializing in that type of problem-solving. This is in contrast to the currently dominant expert system paradigm which proposes a common knowledge base accessed by knowledge-free problem-solvers of various kinds. In novice, these expert structures are often incomplete, and other knowledge structures and learning processes are needed to construct and complete them.

In this article I wish to address some of the problems that confront AI. I am giving, no doubt, what amounts to no more than one man's opinion. It is my hope, in expressing these opinions, that the issues begin to be discussed in some public forum. I will attempt to start this debate by answering some questions about the field that have been posed to me over time. In some cases, what follows are questions that I have simply posed to myself.

THE COGNITION AND PROGRAMMING PROJECT (CAPP) to use such constructs effectively. Dr. Elliot Soloway, Assistant Professor; Dr. Kate which people bring to programming and that computing Ehrlich, Research Associate Lewis Johnson; Jeff Bonar; Valerie Abbott which arise due to cognztively poor programming language constructs. Work is currently in progress on the following projects: What do experts/novices know about programming. 'This project is currently being funded by NSF RISE, under grant'This project is currently being funded by NSF IST, under grant number TIIE AI MAGAZINE Winter/Spring 1083 17 then many individuals will not be able to acquire such languages; Soloway, E., Woolf, B., Rubin, E., Bonar, J. (1982) Overview moreover, it appears beneficial from a problem solving Vancouver, B.C. the empirical projects, we are actively engaged in building an Bonar, J., Ehrlich, K., Soloway, E., Rubin, E. (1982) Collecting AIbased tutoring system, PROUST, which can assist novice Behavioral Research Methods and Instrumentation, this system is to identify non-syntactic bugs in a student's Recent CAPP publications are listed below. What Do Novices Know About Programming?