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) …
A programming language based on sets; motivation and examples. In the introduction to the previous volume, it was mentioned that each future Workshop would include a specially invited address on possible social applications and implications of intelligent machines. The Sixth Machine Intelligence Workshop, the proceedings of which are recorded here, marks the first of this new series. Professor R. L. Gregory presented, in terms both whimsical and profound, his conception of'brain fictions' and their relation to the interface between the worlds of the psychologist and of the engineer. Gregory's message is directed towards whoever is willing himself to question the origins and consequences of new technologies and the transformations of Man's self-picture which they bring in their train.
A technique that has proved useful in shortest path and other discrete optimization computations has been bidirectional search. The method has been well tested in the two-node shortest-path problem providing substantial computational savings. A natural impulse is to extend its benefits to heuristic search. In the unidirectional algorithms, the search proceeds from an initial node forward until the goal node is encountered. Problems for which the goal node is explicitly known can be searched backward from the goal node.
This paper is essentially a state-of-the-art survey of chess programming as typified by the most recent program to appear on the scene. In order to be able to view the situation objectively we feel that it would be useful to preface this with a historical review of the development of ideas in this twenty-year-old field. By considering the most important ideas and techniques that are employed in the (currently) best program available, we hope to convince the reader that progress has been very slow despite the multiplicity of programs (and their associated literature) which have appeared since 1950. The most important paper that has appeared on the subject of computer chess is one written by Claude Shannon in 1948 and published two years later (Shannon 1950). Shannon's paper does not describe an actual program, but offers many suggestions for those who are interested in writing one.
Atm 11177 Stanford Heuristic Programming Project August 1977 Memo HPP-77-25 Computer Science Department Report No. STAN-CS-77-62I THE ART OF ARTIFICIAL INTELLIGENCE: 1. THEMES AND CASE STUDIES OF KNOWLEDGE ENGINEERING by E. A. Feigenbaum COMPUTER SCIENCE DEPARTMENT School of Humanities and Sciences STANFORD UNIVERSITY THE ART OF ARTIFICIAL INTELLIGENCE: I. Themes and Case Studies of Knowledge Engineering STAN-CS-77-621 Heuristic Programming Project Memo 77-25 Edward A. Feigenbaum Department of Computer Science Stanford University Stanford, California ABSTRACT The knowledge engineer practices the art of bringing the principles and tools of Al research to bear on difficult applications problems requiring experts' knowledge for their solution. The technical issues of acquiring this knowledge, representing it, and using it appropriately to construct and explain lines-of-reasoning, are important problems in the design of knowledge-based systems. Various systems that have achieved expert level performance in scientific and medical inference illuminates the art of knowledge engineering and its parent science, Artificial Intelligence. The views and conclusions in this document are those of the author and should not be interpreted as necessarily representing the official policies, either express or implied, of the Defense Advanced Research Projects Agency of the United States Government. This research has received support from the lollowing agencies: Defense Advanced Research Projects Agency, DAHC 15-73-C-0435; National Institutes of Health, 5R24-RR00612, RR-00785; National Science Foundation, MCS 76-11649, DCR 74-23461; The Bureau of Health Sciences Research and Evaluation, HS-01544.
Ph.D. dissertation "Bi-directional and heuristic search in path problems" (Stanford, Computer Science, 1970) summarized in this article in Machine Intelligence 6 (1971).In the uni-directional algorithms, the search proceeds from an initial nodeforward until the goal node is encountered. Problems for which the goal nodeis explicitly known can be searched backward from the goal node. Analgorithm combining both search directions is bi-directional.This method has not seen much use because book-keeping problems werethought to outweigh the possible search reduction. The use of hashingfunctions to partition the search space provides a solution to some of theseimplementation problems. However, a more serious difficulty is involved.To realize significant savings in bi-directional search, the forward andbackward search trees must meet in the 'middle' of the space. The potentialbenefits from this technique motivates this paper's examination of thetheoretical and practical problems in using bi-directional search.