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.

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 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.

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.

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.

The Fairchild Laboratory for Artificial Intelligence Research (FLAIR) was inaugurated in October, 1980, with the purposes of introduction AI Technology into Fairchild Camera and Instrument Corporation, and of broadening the AI base of its parent company, Schlumberger Ltd. The charter of the laboratory includes basic and applied research in all AI disciplines. Currently, we have significant efforts underway in several areas of computational perception, knowledge representation and reasoning, and AI-related architectures. We also engage in various tool-building activities to support our research program. The current computational environment includes several large mainframes dedicated to AI research, a number of high-performance personal scientific machines, and extensive graphics capabilities.

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.

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. Each substructure is in turn further decomposed into a hierarchy of specialist which differ from each other not in the type of problem-solving, but in the conceptual content of their knowledge; e.g.; one of them may specialize in "heart disease," while another may do so in "liver," though both of them are doing the same type of problem solving. Thus ultimately all the knowledge in the system is distributed among problem-solvers which know how to use that knowledge. 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 our framework there is no distinction between knowledge bases and problem-solvers: each knowledge source is a problem-solver. We have so far had occasion to deal with three generic problem-solving types in expert clinical reasoning: diagnosis (classification), data retrieval and organization, and reasoning about consequences of actions. In novice, these expert structures are often incomplete, and other knowledge structures and learning processes are needed to construct and complete them.

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?

One of the challenges increasingly facing intelligence analysts, along with professionals in many other fields, is the vast amount of data which needs to be reviewed and converted into meaningful information, and ultimately into rational, wise decisions by policy makers. The advent of the world wide web (WWW) has magnified this challenge. A key hypothesis which has guided us is that threats come from ideas (or ideology), and ideas are almost always put into writing before the threats materialize. While in the past the'writing' might have taken the form of pamphlets or books, today's medium of choice is themore » WWW, precisely because it is a decentralized, flexible, and low-cost method of reaching a wide audience. However, a factor which complicates matters for the analyst is that material published on the WWW may be in any of a large number of languages. In'Identification of Threats Using Linguistics-Based Knowledge Extraction', we have sought to use Latent Semantic Analysis (LSA) and other similar text analysis techniques to map documents from the WWW, in whatever language they were originally written, to a common language-independent vector-based representation.