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) …
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.
Dr. Lucien Mehl, born 1919 in Paris, studied at the University, Paris where he obtained his degrees in Philosophy and Law, and a Diploma of Advanced Studies in Political Economy and at the National School of Administration. He is now'Maitre des Requetesi to the Council of State and Director of external training at the National School of Administration. He is a member of the International Fiscal Association, the International Cybernetics Association and the French Operational Research Society. He has published a number of articles on administrative science, law, cybernetics and operational research. LUCIEN HEEL INTRODUCTION I. It may seem an ambitious step to try to apply mechanization or automation to the legal sciences.
Common induction systems that construct decision-trees have been reported to operate unsatisfactorily when there are attributes with varying numbers of discrete possible values. This paper highlights the deficiency in the evaluation of the relevance of attributes and examines a proposed solution. An alternative method of selecting an attribute is introduced which permits the use of redundant attributes. Results of experiments on two tasks using the various selection criteria are reported. As knowledge-based expert systems play an increasingly important role in artificial intelligence, more attention is being paid to the problem of acquiring the knowledge needed to build them.
R1, a knowledge-based configurer of VAX-11 computer systems, began to be used over a year ago by Digital Equipment Corporation's manufacturing organization. The success of this program and the existence at DEC of a newly formed group capable of supporting knowledge-based programs has led other groups at DEC to support the development of programs that can be used in conjunction with RI. This paper describes XSEL, a program being developed at Carnegie-Mellon University that will assist salespeople in tailoring computer systems to fit the needs of customers. XSEL will have two kinds of expertise: it will know how to select hardware and software components that fulfil the requirements of particular sets of applications, and it will know how to provide satisfying explanations in the computer system sales domain. The world is filled with tasks that can be performed satisfactory only by those who have acquired, through apprenticeship, the bodies of knowledge relevant to these tasks.
I shall discuss automatic methods of search for solutions in problems susceptible of a particular formal representation, namely that on which the Graph Traverser program (Doran & Michie 1966, and see Doran p. 105) has been based. In Burstall's (see pp. 65-85) problem a solution is defined as a network, the calculated 135 MACHINE LEARNING AND HEURISTIC PROGRAMMING After an initial trial network has been proposed, the program then follows rules invented to allow moves of the following two elementary kinds within the limits set by the security constraints: (1) adding a line joining points i and j for any 10j; (2) deleting a line joining points i and j for any i0j; together with two more obtained as compounds of these; namely (3) two operations of (1) above; (4) two or more operations of (2) above. For any substantial number of points in the network, the number of possible moves which can be generated in these four categories becomes very large, necessitating drastic methods of selection. The way in which this selection is effected is discussed by Burstall (loc. The point I wish to make here is that the basic moves by which neighbouring states are inter-transformable may be unalterably fixed in the structure of the problem as given, or definitions of allowable moves may be imported into the problem in order to give it a structure susceptible to general search methods.
This paper is a report on some preliminary work undertaken as part of a longer term study of the problems which arise in designing and implementing digital computer programs which'learn'. A program has been written which learns to play the board game'Go-Moku' using a particular learning mechanism to be described later. The program is to be regarded as an experimental tool by means of which the particular learning mechanism can be investigated in some depth. Go-Moku is a simple but not a trivial game with an intellectual content comparable with a game of draughts (checkers). Opinions have sometimes been expressed that there is nothing to be learnt (no pun intended!) by programming simple games.
We have de,Jeloped an automated system for the design of laboratory experiments in molecular biology. This paper describes the theory, history, and implementation of the design system and illustrates its function in the domain of DNA cloning experiments. The MOLGEN project is an eight-year collaborative effort among computer scientists and moleculi - biologists at Stanford University to explore computational problem-solving methods within the domain of molecular biology. A fundamental theme of the research has been the application of artificial intelligence methodologies to the problem of experiment design. For an analysis experiment, the plan consists of a series of actions that will elucidate some structural or functional feature of interest.
The method resulted from a study of the problem-solving behavior of scientists which showed that design usually consisted of lookup of abstracted plans followe6 by hierarchical plan-step refinement. Heuristic Programming Project in the Department of Computer Science at Stanford, is researching applications of artificial intelligence to the domain of molecu!ar biology. Among the most interesting and potentially generalizable cognitive problems in molecular biology is the process of experiment design. This involves preparing a plan for an experiment, given synthetic or analytic goals, knowledge about the use of various laboratory tools and techniques. We believe the technique is useful for a wide range of planning problems in many domains.
Reprinted, with permission, from the Proceedings of the IEEE Workshop on Principles of Knowledge-Based Systems, December 1984. John McC3rmott and Allen Newell are at the Carnegie-Mellon University Computer Science Department. Most of this work was done while Paul Rosenbloom and John Laird were also at CMU CSD. Paul Rosenbloom is now at the Stanford University Departments of Computer Science and Psychology. John Laird is now at the Xerox Palo Alto Research Center.
We have developed an automated system for the design of laboratory experiments in molecular biology. This paper describes the theory, history, and implementation of the design system and illustrates its function in the domain of DNA cloning experiments. The MOLGEN project is an eight-year collaborative effort among computer scientists and molecular biologists at Stanford University to explore computational problem-solving methods within the domain of molecular biology. A fundamental theme of the research has been the application of artificial intelligence methodologies to the problem of experiment design. The precise domain of molecular biology under current study is the design of cloning experiments.