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) …
The welcome was given by University of Pittsburgh President Wesley Posvar. The conference cochairmen, Stellan Ohlsson and Jeff Bonar, also gave brief welcomes to the participants. The relatively small size of the conference, about 425 participants, was undoubtedly in part responsible for the congenial ambiance of the meeting. In addition to the opportunity to reunite with old friends, it was easy to establish new relationships with nearly everyone at the conference. With so many attendees from abroad (The Netherlands, Japan, Canada, West Germany, England, Sweden, France, and Hong Kong were all represented by speakers), the international flavor of the conference was well established.
Artificial Intelligence for Microcomputers If you would like to develop an expert system or knowledgebased system on a microcomputer, you might want to read Artijcial Intelligence for Microcomputers by Mickey Williamson, This nontechnical book is easy to understand, written for the unsophisticated microcomputer user. The first chapters provide a brief history of artificial intelligence (AI) and an introduction to natural language query systems. They explain what knowledge-based systems and expert systems are and how they work. Discussions are also provided of the two major AI programming languages, Lisp and Prolog, including their strengths and weaknesses. The remainder of the book is devoted to a review of some of the existing AI software products for microcomputers, such as natural language query systems, decision support systems, expert system development shells, and AI programming languages.
Computer Scaence Department Yale University THE COGNITION AND PROGRAMMING PROJECT (CAPP) in the Computer Science Department at Yale University is an interdisciplinary group exploring a wide range of issues in programming. 'This project is currently being funded by NSF RISE, under grant number SED-81-12403 'This project is currently being funded by NSF IST, under grant number IST-81-14840 We have also shown that when the language construct, agrees with people's natural problem solving strategies they can learn to use such constructs effectively. The implication is that language dcsigners should be more sensitive to cognitive capabilities which people bring to programming and that computing educators should be aware of the systematic misconceptions which arise due to cognztively poor programming language constructs. Using our theory of programming plans, we are developing measures of program complexity that are based on the underlying mental effort needed to understand programs. This approach is in contrast to typical measures of program complexity which are sensitive to only surface features of programs.
This overview of the Yale Artificial Intelligence Project serves as an introduction to Scientific Datalink's microfiche publication of Yale AI Technical Reports Researchers develop new ideas and plant them in programs. The programs are cultivated, hybridized, nurtured. The weaker ideas die out. The stronger ideas are grafted onto new stock and serve as the basis of hearty new strains. At Yale, there has been a traditional summer seminar series at which graduate students present their unprepossessing theories to the vocal and critical review of their colleagues.
The Programmer's Apprentice project uses the domain of programming as a vehicle for studying (and attempting to duplicate) human problem solving behavior. Recognizing that it will be a long time before it is possible to fully duplicate an expert programmer's abilities, the project seeks to develop an intelligent assistant system, the Programmer's Apprentice (PA), which will help a programmer in various phases of the programming task. The Knowledge-Based Editor in Emacs (KBEmacs) is an initial step in the direction of the PA. A question that has been asked about KBEmacs is, "Where's the AI?" Going beyond this, the article uses the development of KBEmacs as an example that illustrates a number of general features of the process of developing an applied AI system. As part of this, the article compares the way AI ideas are used in KBEmacs with the way they were used in the initial proposal for the PA.
The goal of this group is to explore the use of domainspecific knowledge and natural deduction-based reasoning techniques to construct theorem provers that operate in nontrivial mathematical domains. Two new provers, by Larry IIines and Tie-Cheng Wang, are very much like expert systems, since the prover takes its direction by trying to satisfy "higher level" goals, based on knowledge about theorem proving. These are stand-alone provers, not man-machine systems, which are attacking some fairly difficult theorems in mathematics. In addition to this mainline work on mathematical theorem provers, two auxiliary efforts rely heavily on knowledge-based deduction. Michael Starbird is developing a knowledge-based expert system for an area of geometric topology, particularly for three dimensions.
It is written with great intelligence and insight and can benefit a wide audience from advanced undergraduates to seasoned researchers. It is a book that should be in the permanent collection of every AI aficionado because it is such a rich source of ideas and examples. It is not a full-blown AI text; it does not depend on the reader having any previous knowledge of AI but does assume some basic knowledge of Lisp. I have used this book with great success as a supplement in an introductory graduate AI course, the text in a graduate AI course focusing on techniques, and a resource in my research group. The sheer amount of material is impressive, including symbolic mathematics, logic programming, natural language, expert systems, games, and more.
Michael R. Lowry There is substantial evidence that AI technology can meet the requirements of the large potential market that will exist for knowledge-based software engineering at the turn of the century. In this article, which forms the conclusion to the AAAI Press book Automating Software Design, edited by Michael Lowry and Robert McCartney, Michael Lowry discusses the future of software engineering, and how knowledge-based software engineering (KBSE) progress will lead to system development environments. Specifically, Lowry examines how KBSE techniques promote additive programming methods and how they can be developed and introduced in an evolutionary way. The enabling technology will come from AI, formal methods, programming language theory, and other areas of computer science. This technology will enable much of the knowledge now lost in the software development process to be captured in machineencoded form and automated.
Overview The Department of Computer and Information Science (CIS), in the University of Pennsylvania's School of Engineering and Applied Science, conducts a wide range of research projects in artificial intelligence and related disciplines. Participating in this research effort are not only the CIS faculty and students but also members of the chemical, civil, electrical, mechanical, and systems engineering departments; the linguistics, philosophy, and psychology departments; the School of Medicine; and the Wharton School. Primary research interests include natural language processing, knowledge representation, expert systems, automated reasoning and logic programming, analysis and synthesis of motion, computer graphics, graphics animation, program specification and transformation, parallel processing for AI systems, computer vision and robotics, integration of visual and tactile perception, realtime distributed operating systems for vision and robotics, and medical imaging. Facilities available for research computing include VAXes, Symbolics LISP machines, MicroVAXes, Sun and Hewlett-Packard workstations, high-speed graphics displays, and special-purpose vision and robotics equipment. In addition, some parallel computers are expected to be installed later in the year.
Early this year fifty people took an experimental course at Xerox PARC on knowledge programming in Loops During the course, they extended and debugged small knowledge systems in a simulated economics domain called Truckin Everyone learned how to use the Loops environment, formulated the knowledge for their own program, and represented it in Loops At the end of the course a knowledge competition was run so that the strategies used in the different systems could be compared The punchline to this story is that almost everyone learned enough about Loops to complete a small knowledge system in only three days. Although one must exercise caution in extrapolating from small experiments, the results suggest that there is substantial power in integrating multiple programming paradigms. We extend our special thanks to the course participants from Applied Expert Systems, Daisy Systems, ESL, Fairchild AI Lab, Lawrence-Livermore Laboratories, Schlumberger-Doll Research Laboratory, SRI International, Stanford University, Teknowledge, and Xerox Corporation Their participation and feedback are vital to the ongoing experimental process for simplifying the techniques of knowledge programming We enjoyed and will long remember their spirited involvement. As in many situations in life, pat solutions and simple mathematical models just aren't good enough. To cope with messiness, AI researchers have found that large amounts of problem-specific knowledge are usually needed.