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) …
This is an informal description of my ideas about using formal logic as a tool for reasoning systems using computers. Introduction The title of this paper contains both the words'mechanized' and'theory'. I want to make the point that the ideas presented here are not only of interest to theoreticians. I believe that any theory of interest to artificial intelligence must be realizable on a computer. I will not present difficult examples.
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.
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.
The idea of this theorem is that since it is easier to count than to construct the proofs of complicated theorems, this metatheorem can save you the work of generating a proof. More detailed examples will be given in the next section. FOL we introduce a special LS pair mErA. This allows a user to assert many other things about a particular theory. Several examples will be given below.
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.
Human programmers seem to know a lot about programming. This suggests a way to try to build automatic programming systems: encode this knowledge in some machine-usable form. In order to test the viability of this approach, knowledge about elementary symbolic programming has been codified into a set of about four hundred detailed rules, and a system, called PECOS, has been built for applying these rules to the task of implementing abstract algorithms. The implementation techniques covered by the rules include the representation of mappings as tables, sets of pairs, property list markings, and inverted mappings, as well as several techniques for enumerating the elements of a collection. The generality of the rules is suggested by the variety of domains in which PECOS has successfully implemented abstract algorithms, including simple symbolic programming, sorting, graph theory, and even simple number theory.
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.
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!.
"... software technology has promulgated a series of failed promises.... In fact...software bugs are pervasive, and there are no robust platforms underneath." Here we present a number of Elementary Adaptive Modules (EAMs), treating them as the basic building blocks of adaptive agent systems, with a discussion of their use, their control, and their behaviors under different conditions; we will also discuss the host of problems that we expect to run into. We want to explore how to write software that can improve itself, and keep improving itself, far beyond mere simple programmed adaptation of pre-specified parameters. We want to test heuristically how to build and use hierarchical adaptive control structures in software.