If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
APPLICATION OF THEOREM PROVING TO PROBLEM SOLVING *t Cordell Green Stanford Research Institute Menlo Park, California Abstract This paper shows how an extension of the resolution proof procedure can be used to construct problem solutions. The extended proof procedure can solve problems involving state transformations. The paper explores several alternate problem representations and provides a discussion of solutions to sample problems including the "Monkey and Bananas" puzzle and the "Tower of Hanoi" puzzle. The paper exhibits solutions to these problems obtained by QA3, a computer program based on these theorem-proving methods. In addition, the paper shows how QA3 can write simple computer programs and can solve practical problems for a simple robot.
In this paper we describe some major new additions to the STRIPS robot problem-solving system. The first addition is a process for generalizing a plan produced by STRIPS so that problem-specific constants appearing in the plan are replaced by problem-independent parameters. The generalized plan, stored in a convenient format called a triangle table, has two important functions. The more obvious function is as a single macro action that can be used by STRIPS-- either in whole or in part--during the solution of a subsequent problem. Perhaps less obviously, the generalized plan also plays a central part in the process that monitors the real-world execution of a plan, and allows the robot to react "intelligently" to unexpected consequences of actions.
Carnegie-Mellon University, Pittsburgh, Pennsylvania 15213 The Hearsay-H system, developed during the DARPAsponsored five-year speechunderstanding research program, represents both a specific solution to the speechunderstanding problem and a general framework for coordinating independent processes to achieve cooperative problem-solving behavior. As a computational problem, speech understanding reflects a large number of intrinsically interesting issues. Spoken sounds are achieved by a long chain of successive transformations, from intentions, through semantic and syntactic structuring, to the eventually resulting audible acoustic waves. As a consequence, interpreting speech means effectively inverting these transformations to recover the speaker's intention from the sound. At each step in the interpretive process, ambiguity and uncertainty arise.
Marvin Lee Minsky was born in New York on 9th August, 1927. He received his B.A from Harvard in 1950 and Ph.D in Mathematics from Princeton in 1954. For the next three years he was a member of the Harvard University Society of Fellows, and in 1957-58 was staff member of the M.I.T. Lincoln Laboratories. At present he is Assistant Professor of Mathematics at M.I.T. where he is giving a course in Automata and Artificial Intelligence and is also staff member of the Research Laboratory of Electronics. Particular attention is given to processes involving pattern recognition, learning, planning ahead, and the use of analogies or?models!.
Reprinted front the PROCEEDINGS OF THE WESTERN JOINT COMPUTER CONFERENCE Los Angeles, California, March 1955 PRINTED IN THE U.S.A. E CONSIDER the process we call Pattern Recognition. By this we mean the extraction of the significant features of data from a background of irrelevant detail. What we are interested in is simulating this process on digital computers. We give examples on three levels of complexity corresponding to the subjects of the other three speakers here today. We examine in detail the problem on the second level, visual recognition of simple shapes.
THIRD LONDON SYMPOSIUM Papers read at a Symposium on'Information Theory' held at the Royal Institution, London, September 12th to 16th 1955 Published by BUTTERWORTHS SCIENTIFIC PUBLICATIONS 88 KINGSWAY, LONDON, W.C.2 33 PATTERN RECOGNITION AND LEARNING* Massachusetts Institute of Technology, U.S.A. MANY psychologists studying learning have assumed that the subject--rat, dog, or graduate student--invariably knows what the stimulus is. They have not concerned themselves with how a dog knows that it is the bell ringing which is the stimulus to jump over a fence. A bell ringing never gives the same set of nervous impulses into the brain twice (of course the argument would still apply even if it did); why then should the dog classify all cases of bell ringing into one category--'stimulus'? There is then the further question of how this category is more or less quickly'associated' with a response: the point is that the stimulus is not a priori considered a significant entity by the subject. In designing programmes for computers to imitate conditioned reflexes, for instance, we have found that the real problem was to identify the stimulus.
The Role of Experiences and Examples in Learning Systems Edwina L. Rissland Oliver G. Selfridge Elliot M. Soloway* Department of Computer and Information Science University of Massachusetts Amherst, MA 01003 Abstract In this paper, we discuss the role of experiences and examples in learning systems. We discuss these issues in the context of three systems in particular: Rissland and Soloway's Constrained Example Generation (CEG) System, Selfridge's COUNT, and Soloway's BASEBALL. Examples provide the basis from which generalizations, concepts and conjectures are made. They also provide the criticisms needed to refute and refine. For instance, in Winston's learning program [Winston 1975], examples of the concept to be learned, e.g., an arch, and non-examples, e.g., "near misses", are the critical input from which his program builds a structural description of a concept.
A programming language needs simple and well defined semantics. The two favoured theoretical bases for languages have been lambda calculus as advocated by Landin and others, and predicate calculus as advocated by Kowalski (see Landin (1966) and Kowalski (1973)). In this paper I adopt an approach based on predicate calculus, but in a manner that differs from the existing PROLOG language (Warren 1975 and Battani & Meloni 1973) in that I adopt a "forward inference" approach -- inferring conclusions from premises, rather than the "backward inference" approach of PROLOG, which starts with a desired conclusion and tries to find ways of inferring it. This difference is reflected in the internal structure of the associated implementations, that of PROLOG being a "backtrack search" kind of implementation, while the most obvious implementation of the system proposed here involves a kind of mass operation on tables of data, reminiscent of APL (Iverson 1962) but in fact identical in many respects with the work of Codd (Codd 1970) on relational data bases. Indeed, from one perspective this paper can be seen as an extension of Codd's work into the realm of general purpose computing.
Research in machine vision is an important activity in artificial intelligence laboratories for two major reasons. First, understanding vision is a worthy subject for its own sake. The point of view of artificial intelligence allows a fresh new look at old questions and exposes a great deal about vision in general, independent of whether man or machine is the seeing agent. Second, the same problems found in understanding vision are of central interest in the development of a broad theory of intelligence. Making a machine see brings one to grips with problems like that of knowledge interaction on many levels and of large system organization.
In attaining these objectives certain other desirable features of programming languages had to be relegated to secondary importance: (vi) Fast arithmetical facilities on integer and real numbers. Naturally whether (iii) or (vi) and (vii) are attained is to a considerable extent a matter of implementation. The following main features are provided. Roughly analogous features of some other programming languages are mentioned in brackets as a guide: (i) Variables (cf. ALGOL but no types#at compile time).