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Nils J. NILSON
Menlo Park, California 94025, USA
This paper is survey of Artificial Intelligence (AI). It divides the field into four core topics (ambodying the base for a science of intelligence) and eight applications topics (in which research has been contributing to core ideas). The paper discusses the history, the major landmarks, and some of the controversies in each of these twelve topics. Each topic is represented by a chart citing the major references. The se references are contained in an extensive bibliography. The paper concludes with a discussion of some of the criticisms of AI and with some predictions about the course of future research.
Can we ever hope to understand the nature of intelli-
way in which he views himself. In this paper, I
'The field of Artificial Intelligence (AI) has as its
Before beginning we must discuss an important char-
On reflection, this is not surprising. When a field
These are the energing beliefs of a group of computer
Destined apparently to lack an applied branch, is
Whether the activities of these workers constitute a
Or will the science of Al be more 11ke the whole of
presont central ideas sau more specific than does the scientific method but less concrete than DNA.
the applications areas themselves. Until all of the principles of intelligence are uncovered, AI researchers will continue to search for then in various first-level applications areas.
WHAT IS HAPPENING IN AI?
2.1 The structure of the field
As a tactic in attempting to discover the basic principles of intelligence, Al researchers have set themselves the preliainary goal of building computer progrus that can perfon various intellectual tasks that humans can perfon. There are major projects currently under way whose goals are to understand natural language (both written and spoken), play naster chess, prove non- trivial nathematical theoreus, vite. computer programs, and so forth. These projects serve two purposes. First, they provide the appropriate settings in which the basic mechanisas of intelligence can be discovered and clarified. Second, they provide non-trivial opportunities for the application and testing of such mechanisms that are already known. I am calling these projects the first-level applications of Al.
Figure 1, then, divides work in Al into twelva major topics. I have attempted to show the major papers, projects, and results in each of these topics in Charts 1 through 12, each containing references to an extensive bibliography at the end of this paper. These charts help organize the literature as well as indicate some thing about the structure of work in the field. By arrows linking boxes within the charts we attempt to indicate how work has built on (or has been provoked by) previous work. The items in the bibliography are coded to indicate the subheading to which they belong. I think that the charts (taken as a whole) fairly represent the important work even though there may be many differences of opinion among workers about some of the entries* (and especially about how work has built on previous work).
Obviously, a short paper cannot be exhaustive. But in this section I will summarize what is going on in AI research by discussing the major accomplishments and status of research in each of the twelve subheadings.
I have grouped these first-level applications (somewhat arbitrarily) into eight topics shown spread along the periphery of Figure 1. These are the eight that I think have contributed the most to our basic understanding of intelligence. Each has strong ties to other (non-AI) fields, as well as to each other; the major external ties are indicated by arrows in Figure 1.
2.2 The core topics
Basić mechanisms of intelligence and implementational techniques that are common to several applications, I call core topics, It seems to me that there are four major parts to this central core:
Fundamentally, AI is the science of knowledge--how to represent knowledge and how to obtain and use knowledge. Our core topics deal with
these fundamentals. The four topics are highly interdependent, and the reader should be warned that it is probably wrong to attempt to think of them separately even though we are forced to write about them separately.
2.2.1 Common-sense reasoning, deduction, and
problem-solving (Chart 1)
• Techniques for modeling and representation of
knowledge. Techniques for common sense reasoning, deduction,
and problem solving. • Techniques for heuristic search. • AI systems and languages.
These four parts are shown at the center of Figure 1. Again, we have indicated ties to other fields by arrows. It must be stressed that most AI research takes place in the first-level applications areas even though the primary goal may be to contribute to the more abstract core topics.
By reasoning, etc., we mean the major processes involved in using knowledge: Using it to make inferences and predictions, to make plans, to answer questions, and to obtain additional knowledge. As a core topic, we are concerned mainly with reasoning about everyday, common domains (hence, common sense) because such reasoning is fundamental, and we want also to avoid the possible trap of developing techniques applicable only to some specialized domain, Nevertheless, contributions to our ideas about the use of knowledge have come from all of the applications areas.
There have been three major themes evident in this core topic. We night label these puzzle-solving, question-answering, and common-sense reasoning.
If an application is particularly successful, it Bight be noticed by specialists in the application area and developed by then as a useful and economically viable product. Such applications we might call second-level applications to distinguish them from the first-level applications projects undertaken by the Al researchers themselves. Thus, when Al researchers work on a project to develop a prototype system to understand speech, I call it a firstlevel application. II General Motors were to develop and install in their assembly plants a system to interpret television inages of automobile parts on a conveyor belt, I would call it a secondlevel application. (We should humbly note that perhaps several second-level applications will emerge without benefit of obvious Al parentage. In fact, these may contribute nightily to Al science itsell.)
Puzzle-solving. Early work on reasoning concentrated on writing computer prograns that could solve simple puzzles (tower of Hanoi, missionaries and cannibals, logic problems, etc.). The Logic Theorist and GPS (see Chart 1) are typical examples. From this work certain problem-solving concepts were developed and clarified in an uncluttered atmosphere. Among these were the concepts of heuristic search, problem spaces and states, operators (that transformed one problem state into ano ther), goal and subgoal states, meansends analysis, 'and reasoning backwards. The fact
Thus, even though I agree that Al is a field that cannot retain its applications, it is the secondlevel applications that it lacks. These belong to
In particular, some might reasonably clain machine vision (or more generally, perception) and language understanding to be core topics,