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.

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.

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.

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.

In Al knowledge representation schemes, structures that describe other structure:: are said to represent "meta-knowledge." Knowledge about other knowledge can be either about the form of the representation scheme itself (e.g., its syntax) or about the "facts" that are represented (their origin, reliability, Importance, etc.). After reviewing the use of explicit meta-knowledge In several systems, some studies of human behavior that Indicate people's ability to reason about what they know and about how they reason are described. The concept of meta-level knowledge captures intrinsic, commonplace properties of human cognition that are central to an understanding of memory and Intelligence. The use of meta-knowledge In Al systems like MYCIN, which have reached humanexpert-level performance In complex domains, Is a key breakthrough In the design of "knowledge-based" Intelligent systems. Meta-level knowledge has been used in these systems primarily in the implementation of "introspective" processes: Acquisition of new knowledge and explanation of the system's reasoning to users. The usefulness of meta-level descriptions for these and other functions has prompted proposals for their incorporation in several new general-purpose representation schemes, like KRL, as described In the next section.

A clinical investigator graphical capabilities which can plot specific parameters for a keeping the records of his study patients on such a system can patient over time 1126]. However, it is in the analysis of stored use the program's statistical capabilities for data analysis.

References 67 October 27, 1977 1 Introduction This application addresses the continuation of research on the applications of artificial intelligence (Al) (1) to experimental molecular genetics. It is an extension of a longstanding effort to cultivate attention to ongoing laboratory research as a domain of explorations in artificial intelligence. Our major effort in this field had been in the DENDRNL project, with analytical organic chemistry as the object discipline.

In collaboration with other members of the MOLGEN project, the author developed a representation system called the "Unit Package" which became operational in July 1977. In some cases (and usually in ignorance), this work has duplicated other representation work that was happening at about the same time. The Unit Package is now being used by several other projects including two away from Stanford. It is written in INTERLISP and runs under the TENEX and TOPS20 operating systems. It is an interactive system for building knowledge-based programs. It also provides a substantial virtual memory so that knowledge bases of several thousand nodes can created without sacrificing the IlTrERLISP environment.

This paper is a "final report" on the first version of the CRYSALIS project. As such, we will summarize the current state of the system and show where we plan to go with it. We have found that a design (in the software eng ineer ing sense) is a valuable tool for the evaluation and augmentation of a program, even when the design is done ex post facto. Using such a design, we discuss the major flaws of the existing system and how to correct them. Finally, we show how the architecture of this system could be useful for certain other task domains.