Theorem Proving Vision Robotics Information Processing Psychology Learning and Inductive Inference Planning and Related Problem-solving Techniques A. Overview There are two main area.

Theorem Proving Vision Robotics Information Processing Psychology Learning and Inductive Inference Planning and Related Problem-solving Techniques A. Natural Language Processing Ovnrview The most common way that human beings communicate Is by speaking or writing In one of the "natural" languages, like English, French, or Chinese. Computer programming languages, on the other hand, seem awkward to humans. These "artificial" languages are designed to have a rigid format, or syntax, so that a computer program reading and compiling code written In an artificial language can understand what the programmer means. In addition to being structurally simpler than natural languages, the artificial languages can express easily only those concepts that are important In programming: "Do this then do that," "See it such and such Is true," etc. The things that can be expressed In a language are referred to as the semantics of the language. The research on understanding natural language described in this section of the Handbook is concerned with programs that deal with the full range of meaning of languages like English.

It is noted that no one method is best with a view to the potential role of automated decision for all applications. However, emphasis is given to the limitations of early work that have made artificial intelligence techniques and knowledge aids in that domain 1611.

Each of Since the early 1970's, researchers on computei-- these programs uses a representation scheme, based medical reasoning have begun to recognize known as production rules [3], to encode the the potential benefits of applying symbolic reasoning medical knowledge used for decision making.

Those of us involved In the creation of the Handbook of Artificial Intelligence, both writers and editors, have attempted to make the concepts, methods, tools, and main results of artificial Intelligence research accessible to a broad scientific and engineering audience. Currently, Al work Is familiar mainly to its practicing specialists and other interested computer scientists. Yet the field Is of growing interdisciplinary interest and practical Importance. With this book we are trying to build bridges that are easily crossed by engineers, scientists in other fields, and our own computer science colleagues. In the Handbook we Intend to cover the breadth and depth of Al, presenting general overviews of the scientific issues, as well as detailed discussions of particular to -hniques and Important Al systems.

The long term practical goal of this work is the creation of a fully automated system for protein structure determination, starting with data collection and ending with an accurate 3-D model of the molecule. Most of the software already exists at the two ends of the process. At the "front end", programs exist for reducing and transforming the x-ray diffraction data, and estimating phases. The result of this processing Is an electron density map (EDM), which gives a blurred view of the electron cloud surrounding the molecule. At the "back end" are a variety of numerical techniques for taking a full or partial model of the protein and iteratively refining the atomic coordinates so that the model is a best compromise between one which best fits the data and one which best matches Ideal stereochemical constraints [Hermans74] [Agarwal77].

Intelligent systems can explore only tiny subsets of their potential external and conceptual worlds. To increase their effective capacities, they must develop efficient forms of representation, access, and operation. In this paper we develop several techniques which do not sacrifice expressibility, yet enable programs to (semi.

Abstract: Techniques for discovering rules by Induction from large collections of Instances are developed. These are based on an Iterative scheme for dividing the Instances Into two sets, only one of which needs to be randomly accessible. These techniques have made It possible to discover complex rules from data bases containing many thousands of Instances. Results of several experiments using them are reported.

We have developed and partially Imp;zmented an "automated consultant" called SACON (Structural Analysis CONsultant), using the EMYCIN system as Its framework. SACON advises non expert engineers in the use of a large, general-purpose structural analysis program. The structure of the knowledge b,:se, including the major concepts used and Inferences drawn by the consultant, is presented. We conclude by making some observations 11 light of this application about the EMYCIN system as a representational vehicle and the process of acquiring knowledge for rule-based systems. Key words: knowledge-based systems, knowledge acquisition, knowledge representation, automated consultant, structural analysis, inference structure. This research was supported by the Defense Advanced Research Projects Agency (ARPA Order No. 2494 Contract No. DAHC15-73-C-0435) and the Air Force Flight Dynamics Laboratory. Reprinted from the Sixth International Joint Conference on Artificial Intelligence, Tokyo, Japan, August 1979. Used by permission of the International Joint Conference on Artificial Intelligence, Inc.; copies of the Proceedings are available from Morgan Kaufmann Publishers, Inc., 95 First Street, Los Altos, CA 94022, USA.

Currently Al work is familiar mainly to Its practicing specialists and other interested computer scientists. Yet tho field is of growing interdisciplinary Interest and practical importance. With this book we are trying to build bridges that are easily crossed by engineers, scientists in other fields, ond our own computer science colleagues. In the Handbook we intend to cover the breadth and depth of Al, presenting general overviews of the scientific issues, as well as detailed discussions of particular techniques and important Al systems. Throughout we have tried to keep In mind the reader who is not a specialist In Al.