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H a L L O F F a M E Academy of Engineering and American Academy of Arts and Sciences Hall of Fame AI’sHall of Fame n 2010, as the part of the celebration of the The task of selecting from such an accom- 25th anniversary of IEEE Intelligent Sys- plished list was an extremely difficult pro- I tems magazine, our editorial and advisory cess, and we proceeded with great care and boards decided to launch the IEEE Intelli- consideration. I would like to express my gent Systems Hall of Fame to express our sincere thanks to all the members of our ed- appreciation and respect for the trailblazers itorial and advisory boards for their great who have made significant contributions to effort in this endeavor. the field of AI and intelligent systems and to It is always exciting to see that there are honor them for their notable impact and in- people with such passion in a field, and we fluence on our field and our society. hope that our Hall of Fame will be a way to When we first began our search for can- recognize and promote creative work and didates, we did not think we would be so progress in AI and intelligent systems. overwhelmed. It quickly became clear that Now, I proudly present the inaugural in- there was an immense number of amaz- duction of the IEEE Intelligent Systems ing, talented individuals conducting rele- Hall of Fame. Congratulations to our first vant and innovative research in the AI and ever Hall of Fame recipients! intelligent systems field across the globe. —Fei-Yue Wang, Editor in Chief JULY/AUGUST 2011 1541-1672/11/$26.00 © 2011 IEEE 5 Published by the IEEE Computer Society IS-26-04-Fame.indd 5 7/6/11 9:50 AM Edward Feigenbaum Edward Albert Feigenbaum is a professor emeritus of computer science at Stanford University and a cofounder of three start-up firms in applied AI: IntelliCorp, Teknowledge, and Design Power. Often called the “father of expert systems,” he founded the Knowledge Systems Laboratory at Stanford University, where the first expert system, DENDRAL, was de- veloped. Feigenbaum completed his BS and a PhD at Carnegie Mellon University. In his 1959 PhD thesis, carried out under the supervision of Herbert Simon, he developed EPAM, one of the first computer models of how people learn. He helped establish Stanford’s SUMEX-AIM national computer facility for applications of AI to medicine and biology. He has been a member of the Board of Regents of the National Library of Medi- cine and was a member of the Board of Directors of Sperry Corporation. He was the second president of the American Association for Artificial Intelligence. Feigenbaum has received the ACM Turing Award and the Feigenbaum Medal of the World Congress on Expert Systems. From 1994 to 1997, he served as US Air Force Chief Scientist, and he won a US Air Force Exceptional Civilian Service Award. He is a member of the National H A L L O F F A M E Academy of Engineering and American Academy of Arts and Sciences. Engineering Knowledge By Alun Preece rguably more than anyone, Ed Feigenbaum was responsi- the technology in intelligent assistants and software “wizards” that step users Able for getting AI out of the lab and into the enterprise. through complex tasks. In the 1990s, there was a great deal of interest in technical as- The key insight behind what he termed expert systems was to pects of knowledge sharing, by means of federated knowledge bases connected to capture valuable problem-solving knowledge in a knowledge the Internet. Organizations remained committed to the idea of capturing and base, such that a machine could perform research and application development. The leveraging their valuable knowledge, and automated inference on it. Typically, the limits of the technology were quickly iden- expert systems technology became one knowledge was captured by means of rules tified, but nevertheless numerous useful element of the emerging sociotechnical or descriptive frames. As a result, signifi- systems were deployed at all levels within knowledge management field. Effective cant advances were made in techniques enterprises—some of which continue to be heuristic problem-solving techniques re- for rich knowledge and information rep- used today. main an important tool within many en- resentation and on efficient methods for In 1994, at the tail-end of the expert terprises, for example, for scheduling and large-scale inference. Indeed, one of Fei- systems boom, Feigenbaum’s work was resource-allocation tasks. We can also see genbaum’s key interests was the use of heu- recognized—jointly with that of Raj components of knowledge-based systems ristic methods for solving computationally Reddy—by an ACM Turing Award for technology and knowledge engineering hard problems. Motivated in part to com- “pioneering the design and construction of methods in diverse areas such as business pete with Japan’s “Fifth Generation Com- large-scale artificial intelligence systems, rules systems, database integrity manage- puter” project, Feigenbaum worked to alert demonstrating the practical importance ment, and Semantic Web ontologies. Ideas companies and organizations to the poten- and potential commercial impact of artifi- from the expert systems field are also evi- tial benefits of knowledge-based systems. cial intelligence technology.” As professor dent in recent high-profile launches such Feigenbaum coined the term knowledge emeritus at Stanford, Feigenbaum has fo- as IBM’s Watson system and the Wolfram engineering to refer to the incorporation of cused interest, as a Board of Trustees mem- Alpha computational knowledge engine. knowledge into software systems in order ber of the Computer History Museum, Ed Feigenbaum will always be associated to perform complex problem-solving tasks. on preserving the history of computer with the 1980s assertion that “knowledge Essentially, knowledge engineering repre- science, and with the Stanford Libraries is power.” Thirty years later, a great many sented the first widespread industrialization on software for building and using digital AI systems developers still agree. of AI. The AI field was transformed dur- archives. Alun Preece is a professor of intelligent sys- ing the years of the expert systems boom, Feigenbaum’s legacy is broad. We can tems in the School of Computer Science and with significant investment both in basic recognize patterns and characteristics of Informatics at Cardiff University, UK. 6 www.computer.org/intelligent IEEE INTELLIGENT SYSTEMS IS-26-04-Fame.indd 6 7/6/11 9:50 AM H A L L O F F A M E John McCarthy John McCarthy is an American researcher in computer science and pio- neer in the field of AI. Now a professor emeritus at Stanford University, he was previously a professor of computer science, the Charles M. Pigott Pro- fessor in the School of Engineering, and director of the Artificial Intelli- gence Laboratory at Stanford University. McCarthy has a BS in mathemat- ics from the California Institute of Technology and a PhD in mathematics from Princeton University. McCarthy was responsible for coining the term artificial intelligence in his 1955 proposal for the 1956 Dartmouth Con- ference. He founded MIT’s AI laboratory (with Marvin Minsky) in 1957 and Stanford’s AI laboratory in 1963. In 1958, he proposed the Lisp pro- gramming language. He was also one of the first to propose and design time-sharing computer systems, and he pioneered mathematical logic to prove the correctness of computer programs. From 1978 to 1986, he de- veloped the circumscription method of nonmonotonic reasoning. He has received the Turing Award for Computing Machinery, National Medal of Science in Mathematics and Computer Science, Benjamin Franklin Medal in Computer and Cognitive Science, the first Research Excellence Award of IJCAI, and the Kyoto Prize of the Inamori Foundation. He is also a member of the American Academy of Arts and Sciences, National Acad- emy of Engineering, and National Academy of Sciences. Logic and Common Sense By Ulrich Furbach ohn McCarthy is one of the fathers of our discipline. He or- was under construction, McCarthy developed the Lisp language based Jganized the famous Dartmouth Conference, where the foun- on symbolic expressions and sym- bolic evaluation. From the begin- dations for the whole field were laid and the term AI was coined. ning, he designed it such that reason- ing systems could make deductions Not only did he define the goals of AI, he also developed and about programs. It is a language of great elegance—small and yet pow- provided a broad spectrum of key nonmonotonic reasoning in AI and erful and defined with a clear and tools and methods. logic programming; there are ap- formal semantics. Lisp did not only His first seminal paper “Programs plications in most areas of AI. In become the major AI programming with Common Sense” was published his 1969 paper “Some Philosophi- language, it also was the starting before his famous Lisp paper and is cal Problems from the Standpoint of point for numerous papers on the still a pleasure to read about logical Artificial Intelligence,” together with mathematical foundation of comput- representation and reasoning from Pat Hayes he included topics from ing. John McCarthy was probably 1959. This was probably the first paper robot action and planning. It even ad- the first to give a formal mathemati- in which logic as a representation for- dressed the topic of free will. This is cal proof of a compiler for arithmetic malism was proposed. Other papers certainly the first detailed definition expressions. followed on this topic, and by 1970, of situation calculus, which is used in In addition to all these contribu- he developed a modal-logic-based se- so many variants for cognitive robot- tions to programming languages, mantics of knowledge—an issue that ics. This paper also advocated a logic logic, knowledge representation, and is central in symbolic knowledge rep- of knowledge by discussing various planning, John McCarthy published resentation and in Web Science today.
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