The award will be presented at the Eighth International Joint Conference on Artificial Intelligence, to be held in Karlsruhe, West Germany, from 8 to 12 August, 1983. The IJCAI Distinguished Service Award was established in 1979 by the IJCAI Trustees to honor senior scientists in artificial intelligence for contributions and service to the field during their careers. The Award carries a stipend of $1,000 and covers expenses of the recipient's attendance at IJCAI. This will be the second IJCAI Distinguished Service Award; the first was presented to Bernard Meltzer in 1979. Arthur Samuel is one of the pioneeers in AI.

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.

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.

This paper argues for the position that experimental human studies are relevant to most facets of AI research and that closer ties between AI and experimental psychology will enhance the development of booth the principles of artificial intelligence and their implementation in computers. Raising psychological assumptions from the level of ad hoc intuitions to the level of systematic empirical observation, in the long run, will improve the quality of AI research and help to integrate it with related studies in other disciplines.

Production rules are a popular representation for encoding heuristic knowledge in programs for scientific and medical problem solving. However, experience with one of these programs, mycin, indicates that the representation has serious limitations: people other than the original rule authors find it difficult to modify the rule set, and the rules are unsuitable for use in other settings, such as for application to teaching. These problems are rooted in fundamental limitations in mycin's original rule representation: the view that expert knowledge can be encoded as a uniform, weakly structured set of if/then associations is found to be wanting. To illustrate these problems, this paper examines mycin's rules from the perspective of a teacher trying to justify them and to convey a problem-solving approach. We discover that individual rules play different roles, have different kinds of justifications, and are constructed using different rationales for the ordering and choice of premise clauses.

In Memoriam: John G. Gaschnig

Two closely related aspects of artificial intelligence that have received comparatively little attention in the recent literature are research methodology, and the analysis of computational techniques that span multiple application areas. We believe both issues to be increasingly significant as Artificial Intelligence matures into a science and spins off major application efforts. Similarly, awareness of research methodology issues can help plan future research buy learning from past successes and failures. We view the study of research methodology to be similar to the analysis of operational AI techniques, but at a meta-level; that is, research methodology analyzes the techniques and methods used by the researchers themselves, rather than their programs, to resolve issues of selecting interesting and tractable problems to investigate, and of deciding how to proceed with their investigations.

In this article we discuss a method for learning useful conditions on the application of operators during heuristic search. Since learning is not attempted until a complete solution path has been found for a problem, credit for correct moves and blame for incorrect moves is easily assigned. We review four learning systems that have incorporated similar techniques to learn in the domains of algebra, symbolic integration, and puzzle-solving. We conclude that the basic approach of learning from solution paths can be applied to any situation in which problems can be solved by sequential search. Finally, we examine some potential difficulties that may arise in more complex domains, and suggest some possible extensions for dealing with them.

John was best known lately for his work on expert systems, for conversations that helped calibrate my mental compass. He was enthusiastic about and welcomed the added strength that he gave me. Without John, our laboratory is noticeably less than it achieve something truly important. We are proud to have been his colleagues and fortunate John's attitude about my counterarguments was that they I wish he were still here to overcome them.