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) …
Program synthesis is the systematic derivation of a program from a given specification. A deductive approach to program synthesis is presented for the construction of recursive programs. This approach regards program synthesis as a theorem-proving task and relies on a theorem-proving method that combines the features of transformation rules, unification, and mathematical induction within a single framework. MOTIVATION The early work in program synthesis relied strongly on mechanical theoremproving techniques. More recently, program synthesis and theorem proving have tended to go their separate ways.
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.
Knowledge about a particular type of ore deposit is encoded in a computational model representing observable geological features and the relative significance thereof. Following the initial design of a model, simple performance evaluation techniques are used to assess the extent to which the performance of the model reflects faithfully the intent of the model designer. These results identify specific portions of the model that might benefit from "fine tuning", and establish priorities for such revisions. This description of the Prospector system and the model design process serves to illustrate the process of transferring human expertise about a subjective domain into a mechanical realization. I. INTRODUCTION In an increasingly complex and specialized world, human expertise about diverse subjects spanning scientific, economic, social, and political issues plays an increasingly important role in the functioning of all kinds of organizations.
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" ...
Meta-DENDRAL programs are products of a large, interdisciplinary group of Stanford University scientists concerned with many and highly varied aspects of the mechanization of scientific reasoning and the formalization of scientific knowledge for this purpose. An early motivation for our work was to explore the power of existing Al methods, such as heuristic search, for reasoning in difficult scientific problems . DENDRAL project began in 1965. Then, as now, we were concerned with the conceptual problems of designing and writing symbol manipulation programs that used substantial bodies of domain-specific scientific knowledge. In contrast, this was a time in the history of AI in which most laboratories were working on general problem solving methods, e.g., in 1965 work on resolution theorem proving was in its prime.
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.
Dr. W. Ross Ashby, born in London, studied medicine at Cambridge and London, took M.B., B.Ch. He has since been engaged in research in psychiatry, specialising in the application of generalised dynamic principles (equilibrium, homeostasis, self-repairing systems). His present interests are: study of complex equilibria, especially in their topological aspects, as applied to the intelligent and adaptive aspects of the brain. He is now in the Department of Research of Barnwood House Hospital, Gloucester. W. ROSS ASHBY SUMMARY THE Phenomenon of habituation, in which the response to any regularly repeated stimulus decreases, has not so far received any general mechanistic explanation.
For the past several years research on robot problem-solving methods has centered on what may one day be called'simple' plans: linear sequences of actions to be performed by single robots to achieve single goals in static environments. Recent speculation and preliminary work at several research centers has suggested a variety of ways in which these traditional constraints could be relaxed. In this paper we describe some of these possible extensions, illustrating the discussion where possible with examples taken from the current Stanford Research Institute robot system. A major theme in current artificial intelligence research is the design and construction of programs that perform robot problem solving. The usual formulation begins with the assumption of a physical device like a mechanical arm or a vehicle that can use any of a preprogrammed set of actions to manipulate objects in its environment.