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) …
EPISTEMOLOGICAL PROBLEMS OF ARTIFICIAL INTELLIGENCE John McCarthy Computer Science Department Stanford University Stanford, California 94305 Introduction In (McCarthy and Hayes 1969), we proposed dividing the artificial intelligence problem into two parts - an epistemological part and a heuristic part. This lecture further explains this division, explains some of the epistemological problems, and presents some new results and approaches. The epistemological part of Al studies what kinds of facts about the world are available to an observer with given Opportunities to observe, how these facts can be represented in the memory of a computer, and what rules permit legitimate conclusions to be drawn from these facts. It leaves aside the heuristic problems of how to search spaces of possibilities and how to match patterns. Considering epistemological problems separately has the following advantages: I. The same problems of what information is available to an observer and what conclusions ...
Circumscription formalizes such conjectural reasoning. McCarthy  proposed a program with common sense' that would represent what it knows (mainly) by sentences in a suitable logical language. It would decide what to do by deducing a conclusion that it should perform a certain act. Performing the act would create a new situation, and it would again decide what to do. This requires representing both knowledge about the particular situation and general common sense knowledge as sentences of logic.
The frame problem arises in attempts to formalise problem--solving processes involving interactions with a complex world. It concerns the difficulty of keeping track of the consequences of the performance of an action in, or more generally of the making of some alteration to, a representation of the world. The paper contains a survey of the problem, showing how it arises in several contexts and relating it to some traditional problems in philosophical logic. In the second part of the paper several suggested partial solutions to the problem are outlined and compared. This comparison necessitates an analysis of what is meant by a representation of a robot's environment.
John McCarthy, born at Boston, Mass. in 1927, received his B.S. degree in mathematics at the California Institute of Technology in 1948, and his Ph.D. also in mathematics at Princeton University in 1951. He is at present Assistant Professor of Communication Sciences at the Massachusetts Institute of Technology. His present interests are in the artificial intelligence problem, automatic programming and mathematical logic. He is co-editor with Dr. C. E. Shannon of "Automatic Studies". However, certain elementary verbal reasoning processes so simple that they can be carried out by any non--feeble--minded human have yet to be simulated by machine programs.
Oliver Selfridge Using a knowledge-based architecture for a design automation application Facing Web content distribution challenges What makes a compelling empirical evaluation? Driven by his curiosity about the nature of learning, Oliver Selfridge has spent over a half century enmeshed in the most exciting developments in artificial intelligence, communications, and computer science. A participant at the original conference at Dartmouth in 1956 (and at the Western Joint Computer Conference in Los Angeles the year before, which he considers the true start of Al), Selfridge formed working relationships and cemented friendships with AI's founding members--John McCarthy, Marvin Minsky, and Allen Newell, among others--as he went on to become a true AI pioneer himself. Before retiring in 1993 after 10 years as Chief Scientist at GTE Laboratories, Computer and Information Systems Lab, he served as a member of the National Security Agency's Advisory Board for 20 years, chairing its Data Processing Panel for the last 15 of those. He also served on various advisory panels to the White House, as well as on the peer review committee for the National Institute of Health (NIH), directed Project MAC and the Cambridge Project at MIT's Lincoln Labs, and was Staff Scientist at Bolt, Beranek, and Neuman (BBN).
The two outstanding figures in the history of computer science are Alan Turing and John von Neumann, and they shared the view that logic was the key to understanding and automating computation. In particular, it was Turing who gave us in the mid-1930s the fundamental analysis, and the logical definition, of the concept of'computability by machine' and who discovered the surprising and beautiful basic fact that there exist universal machines which by suitable programming can be made to Since completing that essay I have had the benefit of extremely helpful discussions on many of the details with Professor Donald Michie and Professor I. J. Good, both of whom knew Turing well during the war years at Bletchley Park. Professor J. A. N. Lee, whose knowledge of the literature and archives of the history of computing is encyclopedic, also provided additional information, some of which is still unpublished. Further light has very recently been shed on the von Neumann side of the story by Norman Macrae's excellent biography John von Neumann (Macrae 1992). Accordingly, it seemed appropriate to undertake a more complete and thorough version of the FGCS'92 essay, focussing somewhat more on the interesting historical and biographical issues.
A robot, in order to act intelligently, must be able to reason from facts which its sensors detect to conclusions which govern its actions. This reasoning process is so central to human intelligence that it seems immediately relevant to the problems of robot design to consider its properties, how it might be analysed and imitated. Obviously these are not the specialities of the most refined thinking. They are the commonplaces of the least refined thinking; and are yet the indispensable core of the conceptual equipment of the most sophisticated human beings. It is with these, their inter-connexions, and the structure that they form, that a descriptive metaphysics will be primarily concerned.'
A computer program capable of acting intelligently in the world must have a general representation of the world in terms of which its inputs are interpreted. Designing such a program requires commitments about what knowledge is and how it is obtained. Thus, some of the major traditional problems of philosophy arise in artificial intelligence. More specifically, we want a computer program that decides what to do by inferring in a formal language that a certain strategy will achieve its assigned goal. This requires formalizing concepts of causality, ability, and knowledge.
Does Probability Have a Place in Nonmonotonic Reasoning? Does Probability Have a Place in Nonmonotonic Reasoning? COMPUTER SCIENCE DEPARTMENT Stanford University Stanford, California 94305 Does Probability Have a Place in Nonmonotonic Reasoning? My intention here is to present some results that deal with these questions. Should probabilities be used to implement nonmonotonic reasoning systems?
Department of Computer'cience Stanford University Stanford, Cahfornta 9405 THE STANFORD Artificial Intelligence Project, later known as the Stanford Al Lab or SAIL, was created by Prof. John McCarthy shortly after his arrival at Stanford in 1962. As a faculty member in the Computer Science Division of the Mathematics Department, McCarthy began supervising research in artificial intelligence and timesharing systems with a few stucents. McCarthy built a large and active research organization involving many other faculty and research projects as well as his own. His advice, "don't try to unify the report" preempted any contrary obligations I felt to readers. I also appreciate time and Information from Ed Feigenbaum.