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) …
Optimal Search Strategies for Speech Understanding Control W. A. Woods Bolt Beranek and Newman Inc. Cambridge, MA 02238 Abstract This paper describes two algorithms for finding the optimal interpretation of an unknown utterance in a continuous speech understanding system. These methods guarantee that the first complete interpretation found will be the best scoring interpretation possible. Moreover, unlike other optimal strategies, they do not make finite-state assumptions about the nature of the grammar for the language being recognized. One of the methods, the density method, is especially interesting because it is not an instance of the "optimal" A* algorithm of Hart, Nilsson, and Raphael, and appears to be superior to it in the domains in which it is applicable. The other method, the shortfall method, is an instance of the A* algorithm using a particular heuristic function.
The selection of what to do next is often the hardest part of resource-limited problem solving. In planning problems, there are typically many goals to be achieved in some order. The goals interact with each other in ways which depend both on the order in which they are achieved and on the particular operators which are used to achieve them. A planning program needs to keep its options open because decisions about one part of a plan are likely to have consequences for another part. This paper describes an approach to planning which integrates and extends two strategies termed the least-commitment and the heuristic strategies.
We learn (memorize) multiplication tables, learn (discover how) to walk, learn (build UP an understanding of, then an ability to synthesize) languages. Many subtasks and capabilities are involved in these various kinds of learning. One capability central to many kinds of learning is the ability to generalize: to take into account a large number of specific observations, then to extract and retain the important common features that characterize classes of these observations. This generalization problem has received considerable attention for two decades in the fields of Artificial Intelligence, Psychology, and Pattern Recognition (e.g., [Bruner, The results so far have been tantalizing: Partially successful generalization programs have been written for problems ranging from learning fragments of spoken English to learning rules of Chemical spectroscopy. But comparing alternative strategies, and developing a general understanding of techniques has been difficult because of differences in data representations, terminology, and problem characteristics.
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.
ABSTRACT In this paper we present a new algorithm for searching trees. It does this by attempting to find both the best arc at the root and the simplest proof, in best-first fashion. This strategy determines the order of node expansion. Any node that is expanded is assigned two values: an upper (or optimistic) bound and a lower (or pessimistic) bound. During the course of a search, these bounds at a node tend to converge, producing natural termination of the search.
This document is a GTE Laboratories Technical Report. It describes results and conclusions reached upon completion of a major phase of a research project. The ideas and views put forth by the author have been rev' we accepted by the appropriate Laboratory Director. We explore the use of adaptive components in strategies for playing extremely simple two-person zero-sum games. The adaptation is different from that usually considered in the field of Adaptive Control; rather it is a form of test and gradient descent.
TRACKING AND TRAILING: ADAPTATION IN MOVEMENT STRATEGIES Introduction "e 4-ure to Call creatures], without profusion, kind, The oroper organ, proper powers assigned; Eecn seeming want compensated of course, e:e with egree of sittness, there of force Scientists and engineers can, I suppose, take heart from?one's opti7is7; and Tathematicians can revel in his promise of linearit7, "force/11 in exact Proportion." To discover what are the proPer organs and taa proper powers, and at has been the nature of the compensation, we need to deal with the complexity of organization and feedback. This may seem to fly in the face of Occam's razor, but sim7)le strategies can produce complex behavior, and some simple behavior may in fact be the not so simple product of interactin7 strategies. There there are common processes at,woCk we should find and (r_:escrie them. This monograph studies the adaptive nature of tracking -- following tracs and trails. It 7,s'ks what an organism needs to know in order ...
SOME THEMES AND PRIMITIVES IN ILL-DEFINED SYSTEMS* Oliver G. Sel fr idge /45 Percy Road Lexington, MA 02173 To say that something is ill-defined is more to describe us than it. That is, for sane system we are dealing with, we are more or less ignorant of its working, and that means that we have special problems in trying to control it. Nevertheless, of course, most of the real systems we deal with are ill-defined in that sense -- like other people. But we do find ways to exercise some degree of control. The main theme here is that -- regardless of exactly what control means -- it is feasible to do better by a succcession of quite small improvements in a strategy.
We discuss several aspects of legal arguments, primarily arguments about the meaning of statutes. First, we discuss how the requirements of argument guide the specification and selection of supporting cases and how an existing case base influences argument formation. This taxonomy builds upon our much earlier work on'argument moves' and also on our more recent analysis of how cases are used to support arguments for the interpretation of legal statutes. Third, we show how the theory of argument used by CABARET, a hybrid case-based/rule-based reasoner. Selecting the best cases possible is crucially important to advancing one's interests, especially in an adversarial domain such as law that requires advocates to support their positions with previous cases.
The study of perception is divided among many established sciences: physiology, experimental psychology and machine intelligence; with several others making contributions. But each of the contributing sciences tends to have its own concepts, and ways of considering problems. Each -- to use T. S. Kuhn's term (1962) -- has its own'paradigm', within which its science is respectable. This can make cooperation difficult, as misunderstandings (and even distrust) can be generated by paradigm differences. This paper is a plea to consider perceptual phenomena from many points of view, and to consider whether a general paradigm for perception might be found.