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) …
In the synthesis of a plan or computer program, the problem of achieving several goals simultaneously presents special difficulties, since a plan to achieve one goal may interfere with attaining the others. This paper develops the following strategy: to achieve two goals simultaneously, develop a plan to achieve one of them and then modify that plan to achieve the second as well. A systematic program modification technique is presented to support this strategy. The technique requires the introduction of a special "skeleton model" to represent a changing world that can accommodate modifications in the plan. This skeleton model also provides a novel approach to the "frame problem."
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 ...
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.
SESSION 4B PAPER 3 TO WHAT EXTENT CAN ADMINISTRATION BE MECHANIZED? Mr. J. H. H. Merriman was educated at King's College School, Wimbledon, and King's College, University of London. He obtained his B.Sc. (Hons.) in 1935 and did Postgraduate Research at King's College London obtaining his M.Sc. Engineering Department, Radio Research Branch, Dollis Hill, in 1936 and was associated with development of long distance radio communication systems. He was Officer-in-charge Castleton radio research station 1940-8, and from 1948-5 in the Office of Engineer-in- Chief G.P.O. and responsible for microwave system development and planning.
Lectured in Philosophy at the Hebrew University In Jerusalem and became Associate Professor in 1957. Since 1957 he has also taught in the Department of History and Philosophy of Science. Joint author with Professor A. A. Fraenkel of "Foundations of Set Theory", to be published by the North-Holland Publishing Company in the series "Studies In Logic". Y. BAR-HILLEL SUMMARY "FOUR sources of inefficiencies in the process of literature searching are briefly described. An "Ideal" solution Is outlined as a frame of reference and its shortcomings discussed.
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!.
Reprinted from Information Theory, Fourth London Symposium published by Butterworths, 88 Kingsway, London, W.C.2. MARVIN MINSKY and OLIVER G. SELFRIDGE Lincoln Laboratory*, Massachusetts Institute of Technology INTRODUCTION THE general nature of the problem is that an organism must learn to make the'right', or appropriate, response to its inputs. Typically, the inputs are large amounts of data, so that the machine must learn to recognize the similarities between different inputs which call for the same response, contrasted with the distinctions that call for different responses. The particular machines we are concerned with are random nets. A random net is a large set of similar and simply-acting elements whose attributes and interactive connections may be randomly established.
We discuss several aspects of legal arguments, primarily arguments about the meaning of statutes. First, we discuss how the requirements of argument guide the specification and selection of supporting cases and how an existing case base influences argument formation. This taxonomy builds upon our much earlier work on'argument moves' and also on our more recent analysis of how cases are used to support arguments for the interpretation of legal statutes. Third, we show how the theory of argument used by CABARET, a hybrid case-based/rule-based reasoner. Selecting the best cases possible is crucially important to advancing one's interests, especially in an adversarial domain such as law that requires advocates to support their positions with previous cases.
Rules often contain terms that are ambiguous, poorly defined or not defined at all. In order to interpret and apply rules containing such terms, appeal must be made to their previous constructions, as in the interpretation of legal statutes through relevant legal cases. We describe a system CABARET (CAse-BAsed REasoning Tool) that provides a domain-independent shell that integrates reasoning with rules and reasoning with previous cases in order to apply rules containing ill-defined terms. The integration of these two reasoning paradigms is performed via a collection of control heuristics, which suggest how to interleave case-based methods and rule-based methods to construct an argument to support a particular interpretation. CABARET is currently instantiated with cases and rules from an area of income tax law, the so-called "home office deduction".
This paper presents a hybrid case-based reasoning (CBR) and information retrieval (IR) system, called SPIRE, that both retrieves documents from a full-text document corpus and from within individual documents, and locates passages likely to contain information about important problem-solving features of cases. SPIRE uses two case-bases, one containing past precedents, and one containing excerpts from past case texts. Both are used by SPIRE to automatically generate queries, which are then run by the INQUERY full-text retrieval engine on a large text collection in the case of document retrieval and on individual text documents for passage retrieval. A good indication of what to look for in a new problem situation is often given by examples of what has worked in the past. We have employed this idea at two levels in a hybrid CBR-IR approach: 1. within a corpus of documents, to find documents relevant to a new problem situation, retrieve documents similar to those that are already known to be relevant; 2. within an individual document, to find passages that address a particular aspect of a situation, retrieve passages that are similar to those that illustrate past discussions of the topic.