Knowledge representation is at the very core of a radical idea for understanding intelligence. Instead of trying to understand or build brains from the bottom up, its goal is to understand and build intelligent behavior from the top down, putting the focu
【作者简介】
Ron Brachman has been doing influential work in knowledge representation since the time of his Ph.D. thesis at Harvard in 1977, the result of which was the KL-ONE system, which initiated the entire line of research on description logics. For the majority of his career he served in research management at AT&T, first at Bell Labs and then at AT&T Labs, where he was Communications Services Research Vice President, and where he built one of the premier research groups in the world in Artificial Intelligence. He is a Founding Fellow of the American Association for Artificial Intelligence (AAAI), and also a Fellowof the Association for Computing Machinery (ACM). He is currently President of the AAAI. He served as Secretary- Treasurer of the International Joint Conferences on Artificial Intelligence (IJCAI) for nine years. With more than 60 technical publications in knowledge representation and related areas to his credit, he has led a number of important knowledge representation systems efforts, including the CLASSIC project at AT&T,which resulted ina commercially deployed systemthat processedmore than $5 billion worth of equipment orders. Brachman is currently Director of the Information Processing TechnologyOffice at theU.S.Defense AdvancedResearch Projects Agency (DARPA), where he is leading a new national-scale initiative in cognitive systems. Hector Levesque has been teaching knowledge representation and reasoning at the Univer- sity of Toronto since joining the faculty there in 1984. He has published over 60 research papers in the area, including three that have won best-paper awards. He has also co-authored a book on the logic of knowledge bases and the widely used TELL–ASK interface that he pioneered in his Ph.D. thesis. He and his collaborators have initiated important new lines of research on a number of topics, including implicit and explicit belief, vivid reasoning, new methods for satisfiability, and cognitive robotics. In 1985, he became the first non-American to receive the Computers and Thought Award given by IJCAI. He was the recipient of an E.W.R. Steacie Memorial Fellowship from the Natural Sciences and Engineering Research Council of Canada for 1990–1991. Hewas also a Fellowof the Canadian Institute for Advanced Research from 1984 to 1995, and is a Founding Fellow of the AAAI. He was elected to the Executive Council of the AAAI, and is on the editorial board of five journals. In 2001, Levesque was the Conference Chair of the IJCAI-01 conference, and is currently Past President of the IJCAI Board of Trustees. Brachman and Levesque have beenworking together on knowledge representation and rea- soning for more than 25 years. In their early collaborations at BBN and Schlumberger, they produced widely read work on key issues in the field, as well as several well-known knowledge representation systems, including KL-ONE, KRYPTON, and KANDOR. They presented a tutorial on knowledge representation at the International Joint Conference on Artificial Intelligence in 1983. In 1984, they coauthored a prize-winning paper at the National Conference on Artificial Intelligence that is generally regarded as the impetus for an explosion of work in description logics and which inspired many new research efforts on the tractability of knowledge rep- resentation systems, including hundreds of research papers. The following year, they edited a popular collection, Readings in Knowledge Representation, the first text in the area. With Ray Reiter, they founded and chaired the international conferences on Principles of Knowl- edge Representation and Reasoning in 1989; these conferences continue on to this day. Since 1992, they have worked together on the course in knowledge representation at the University of Toronto that is the basis for this book.
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