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) …
The purpose of this research is to investigate the extent to which knowledge can replace and support search in selecting a chess move and to delineate the issues involved. This has been carried out by constructing a program. PARADISE (PArtern Recognition Applied to Directing SEarch), which finds the best move in tactically sharp middle game positions from the games of chess masters. The actions of the rules post concepts in the data base while the conditions match patterns in the chess position and data base. The program uses the knowledge base to discover plans during static analysis and to guide a small tree search which confirms that a particular plan is best.
The first section discusses the importance of having systems that understand the concept of knowledge, and how knowledge is related to action. Section 2 points out some of the special problems that are involved in reasoning about knowledge, and section $ presents a logic of knowledge based on the idea of possible worlds. Section 4 integrates this with a logic of actions and gives an example of reasoning in the combined system. Section 5 makes some concluding comments. I. Introduction One of the most important concepts an intelligent system needs to understand is the concept of knowledge.
ABSTRACT TEntEsuis is a program designed to provide assistance on the task of building knowledge-based systems. It facilitates the interactive transfer of knowledge from a human expert to the system, in a high level dialog conducted in a restricted subset of natural language. TEIRESIAS in operation and demonstrates how it guides the acquisition of new inference rules. I. Introduction Where much early work in artificial intelligence was devoted to the search for a single, powerful, domain-independent problem solving methodology (e.g., This work was supported in part by the Advanced Research Projects Agency under ARPA Order 2494; by a Chaim Weizmann Postdoctoral Fellowship for Scientific Research, and by grant MCS 77-02712 from the National Science Foundation. It was carried out on the SUMEX Computer System, supported by the NIH Grant RR-00785. The program is named for the blind seer in Oedipus the King, since, as we will see, the program, like the prophet, has a form of "higher order" ...
ABSTRACT This talk reviews those efforts in automatic theorem proving, during the past few years, which have emphasized techniques other than resolution. These include: knowledge bases, natural deduction, reduction, (rewrite rules), typing, procedures, advice, controlled forward chaining, algebraic simplification, built-in associativity and commutativity, models, analogy, and man-machine systems. Examples are given and suggestions are made for future work. Earlier work by Newell, Simon, Shaw, and Gelernter in the middle and late 1950s emphasized the heuristic approach, but the weight soon shifted to various syntactic methods culminating in a large effort on resolution type systems in the last half of the 1960s. It was about 1970 when considerable interest was revived in heuristic methods and the use of human supplied, domain dependent, knowledge.
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.
Dr. Francois Paycha, born at Narbonne, studied medicine at the University of Montpellier. His first researches were concerned with the embryology of the eye, later using the distribution of radioactive phosphorus P32 to study the structure of the tissues and for the detection of tumours. He was then appointed to the National Centre of Scientific Research. While in charge of a hospital clinic, he noted the considerable differences in the diagnoses of conscientious and knowledgeable practitioners and those advanced by the hospital. In view of the special need for exact diagnosis in medicine he made a study of the causes of these differences.
Recent activities have swung away from biology, but this will be remedied. THE application of learning machines to process control is discussed. Three approaches to the design of learning machines are shown to have more in common than is immediately apparent. These are (1) based on the use of conditional probabilities, (2) suggested by the idea that biological learning is due to facilitation of synapses and (3) based on existing statistical theory dealing with the optimisation of operating conditions. Although the application of logical-type machines to process control involves formidable complexity, design principles are evolved here for a learning machine which deals with quantitative signal and depends for its operation on the computation of correlation coefficients.
Frank Rosenblatt, born in New Rochelle, New York, U.S.A., July 11, 1928, graduated from Cornell University in 1950, and received a PhD degree in psychology, from the same university, in 1956. He was engaged in research on schizophrenia, as a Fellow of the U.S. Public Health Service, 1951-1953. He has made contributions to techniques of multivariate analysis, psychopathology, information processing and control systems, and physiological brain models. He is currently a Research Psychologist at the Cornell Aeronautical Laboratory, Inc., in Buffalo, New York, where he Is Project Engineer responsible for Project PARA (Perceiving and Recognizing Automaton). FRANK ROSENBLATT SUMMARY A THEORETICAL brain model, the perceptron, has been developed at the Cornell Aeronautical Laboratory, In Buffalo, New York.
Dr. Uttley took an Honours degree in Mathematics at King's College, London where he also took a degree in Psychology and did postgraduate research in Visual Perception. At the Royal Radar establishment he designed and built analogue and digital computers. For the last five years Dr. Uttley has been working on theories of computing in the nervous system. The suggestion is based on the similarity of behaviour of these formal systems and or animals. The design of classification computers is discussed in the first paper; the design of conditional probability computers Is discussed in a third paper (Uttley, 1958, ref. 15); in both papers working models are described.
Dr. MacKay is a lecturer in Physics After graduating from St. Andrew's University in 1943 he spent three years on Radar work with the Admiralty. Since 1946, when he joined the staff of King' s College, he has been active in the development of information theory, with special interest in its bearing on the study of both natural and artificial information-systems. In 1951 a Rockefeller Fellowship enabled him to spend a year working in this field in U.S.A. His experimental work has been mainly concerned at first with highspeed analogue computation, and latterly with the informational organization of the nervous system. D. M. MACKAY SUMMARY THIS paper is concerned with some theoretical problems of securing and evaluating'intelligence' in artificial organisms, - particularly the kind of operational features that distinguish what we call'intellect' from mere ability to calculate. Among those discussed are (a) the ability to take cognizance of the'weight' as well as the structure of Information.