Robert S. (Bob) Engelmore, who retired in 1998 from the Knowledge Systems Laboratory at Stanford University, died in an ocean accident in Hawaii on March 25, 2003. As the second editor of AI Magazine, he guided its development from 1981 to 1991; he was also elected a fellow of AAAI in 1992. He had been involved in many aspects of AI and was respected for his uncommon common sense and good humor. He played football for Briarcliff Manor High School, learned to play the piano, and most importantly nurtured a deep interest in science. He won a nationally prestigious Westinghouse science scholarship to Carnegie Institute of Technology (later Carnegie Mellon University) and became a physics major.

In the first phase of the analysis, I produced a conceptual reconstruc-Peter D. Karp In the next phase, I searched for patterns in the differences between successive My Ph.D. dissertation describes an A class knowledge base defines a tax-states of the biologists' knowledge. Patfocuses on a program of research in process knowledge base describes the terns in the differences indicate reamolecular biology that culminated in chemical reactions that can occur soning methods that were used to the discovery of a new mechanism of between the biological objects in this derive new theories from old ones. An experiment is described My analysis identified theory-modifiuation. In the first phase of my work, in a third knowledge base by creating cation operators that the biologists I performed a historical study of this the particular objects (instantiated used to modify their theories; these program of biological research in from the known classes of objects) operators form the core of the which I reconstructed the different that are present in the experiment. These patterns also supat different points in time and then called Gensim (genetics simulator) port the conjecture that scientists use analyzed the differences between predicts experimental outcomes by four different modes of scientific these successive theories.

Psychological research has revealed that human performance in the face of uncertainty is spotty at best. Moreover, both novices and experts are subject to these kinds of inaccuracies and errors. This poor report card should be particularly distressing to knowledge engineers (KEs) who are confronted with the dilemma that no matter how uncertain knowledge is represented in an expert system, it is suspect if acquired from a human, even a human expert. Those who are trying to automate knowledge acquisition by building intelligent interfaces to knowledge engineering tools cannot be comforted by this news. Their interfaces would have to contain sophisticated and as yet unspecified metaknowledge about these particular human frailties in order to overcome the problem.

Through the technology of expert systems, the expertise of highly skilled personnel can be automated and used to assist lesser skilled personnel in the diagnosis and repair of complex machines. Expert systems that incorporate causal reasoning represent a second-generation approach to the provision of diagnostic assistance. The technology involved performs postdiction by reasoning from first principles. This article is based on research in qualitative physics and the philosophy of causality. A new implementation vehicle for causal reasoning is described, one that embodies hypothetical or counterfactual reasoning (Roach, Eichelman, and Whitehead 1985) in a language called Wif (What IF).

A workshop on high-level connectionist models was held in Las Cruces, New Mexico, on 9-11 April 1988 with support from the American Association for Artificial Intelligence and the Office of Naval Research. John Barnden and Jordan Pollack organized and hosted the workshop and will edit a book containing the proceedings and commentary. The book will be published by Ablex as the first volume in a series entitled Advances in Connectionist and Neural Computation Theory. The two fields are often posed as paradigmatic enemies, and a risk of severing them exists. Few connectionist results are published in the mainstream AI journals and conference proceedings other than those sponsored by the Cognitive Science Society, and many neural-network researchers and industrialists proceed without consideration of the problems (and progress) of AI.

Summary Eurisko is an AI program that learns by discovery We are applying Eurisko to the task of inventing new kinds of three-dimensional microelectronic devices that can then be fabricated using recently developed laser recrystallization techniques Three experiments have been conducted, and some novel designs and design rules have emerged. The paradigm for Eurisko's exploration is a loop in which it. Many of the well-known primitive devices were synthesized quickly, such as the MOSFET, Junction Diode, and Bipolar Transistor. This was unsurprising, as they were short sentences in the descriptive language we had defined (a language with verbs like Abut and ApplyEField, and with nouns like nDopedRegion and IntrinsicChannelRegion) Future We wish to thank those graduate students who have aided us in the construction of RLL, the language in which Eurisko is written, most notably Greg Harris at CMIJ and Russ Grciner at Stanford.

This editorial introduces the articles published in the AI Magazine special issue on Innovative Applications of Artificial Intelligence (IAAI), based on a selection of papers that appeared in the IAAI-05 conference, which occurred July 9-13 2005 in Pittsburgh, Pennsylvania. IAAI is the premier venue for learning about AI's impact through deployed applications and emerging AI application technologies. Case studies of deployed applications with measurable benefits arising from the use of AI technology provide clear evidence of the impact and value of AI technology to today's world. The emerging applications track features technologies that are rapidly maturing to the point of application. The six articles selected for this special issue are extended versions of papers that appeared at the conference.

GLISI' is a high-level language that. is compiled into LISP It provides a versatile abst art,-dnt.at.ypc facility with hierarchical inheritance of pl oprl ties and object,-centered programming GLISP programs are shorter and more readable than equivalent LISP programs The object code produced by GLISP is optimized, making it about as cfflcient as handwritten LISP An integrated programming environment is provided, including automatic incremental compilation, interpretive programming features, and an intelligent display-hased inspector/editor for data and data-type descriptions GLISP code is relatively portahlr; the compiler and the data inspcrtor are implemcntcd for most major dialects of LISI' and arc availablr flee or at nominal cost This research was supported in part by NSF grant SED-7912803 in the Joint National Science Foundation - National Institute of Education Program of Research on Cognitive Processes and the Struct,urr of Knowledge in Science and Mathematics, and in part by the Defense Advanced Resealrh Projects Agency rmdel contract MDA-903-80-c-007 Author's present address: Computer Science Department, University of Texas at Austin, Austin, TX: 78712 GLISP contains ordinary LISP as a sublanguage; LISP code can be mixed with GLISP code, so t,hat no capabilities of the underlying LISP syst,cm arc lost. GLISP has also been ex-!,cndcd as a hardware description language for describing VLSI designs. GLISP Statements GLISP provides several kinds of statements that arc t,ranslated into equivalent, code in 1,ISP; each is identified by a key word at, the front of a list containing the code for the stwtemcnt. Many of these statements are similar to t,hose provided by I'ASC!AL: If..then.else While Repeat Case ...Do ..Until These control st,atements provide A compact Given a. set of name/value pa,irs, the A function creates a new tlat,a st,ruct,urc having t,ht: specified values: (A CIRCLE WITH RADIUS Given the earlier ob.jcct dcscript,ion for CIRCLE, this will compile as: (LIST (APPEND '(0 0)) R) The A function works interpretively as well as wit,hin caonlpiled code Context and Type Inference One of t,hc design goals of CLISP is that program code should be independent of the irnpl T le t,at,ic,rls of the structures manipulated by the code to the grcat,cst, dcgrcc possiblc Inclusion of redundanl t,ype declarations in program code would make the code dependent on the actual inplementwtion of structures; instead, GLISI' relies on type inference and its compile-time context, mechanism to tletcrmine the types of object,s.

The Full-Sized Knowledge-Based Systems Research Workshop was held May 7-8, 1990 in Washington, D.C., as part of the AI Systems in Government Conference sponsored by IEEE Computer Society, Mitre Corporation and George Washington University in cooperation with AAAI. The goal of the workshop was to convene an international group of researchers and practitioners to share insights into the problems of building and deploying Full-Sized Knowledge Based Systems (FSKBSs). The term "full-sized" was chosen to encourage discussion of questions not only of largeness but also of breadth, depth, maturity, and deployment scale. For example, a 1000-rule expert system facilitating knowledge sharing and collaboration between several thousand users was felt to be as interesting to the workshop as a 100,000-rule system with only a few users. That notwithstanding, the underlying question was how to overcome the brittleness and narrowness of the first generation of expert systems, and how to use a variety of new ideas and technologies to increase the scale, intelligence, and capability of the systems currently able to be fielded.

Efficient indexing schemes have influenced the acceptance of production systems in the industrial world. However, in embedded-control systems, production systems have not been applied intensively because of their nondeterministic run-time behavior. Thus, nonpredictability of response times is a major obstacle to the widespread use of expert systems in the real-time domain. Such systems are considered intelligent when they are able to perform complex actions in response to the sensed environment. In intelligent real-time systems, there is a tradeoff between acting and reasoning.