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Peter E. Hart Chairman and Founder Ricoh Innovations, Inc.
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Professional
Focus
- Management of innovation
- Multimedia document analysis and communication
- Information
appliances
Leadership Highlights
Technical
Highlights
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Nearest neighbor decision rule The nearest neighbor rule is a widely-used method in pattern classification. The first derivation of its fundamental theoretical properties were described in: Cover, T. M. and P. E. Hart, "Nearest Neighbor Pattern Classification," IEEE Trans. on Information Theory, Vol. IT-13, No. 1, pp 21-27 (January 1967). Over 5,000 citations of the nearest neighbor rule were found by Citeseer, and our apparently-seminal paper eventually received a Golden Jubilee Paper Award from the IEEE Information Theory Society. |
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| The A* algorithm for finding the shortest path through a graph The A* algorithm is guaranteed to find the shortest path through a graph, and does so with minimum computation. It was first described, and its admissibility and optimality properties proved, in: Hart, P. E., N. J. Nilsson, and B. Raphael, "A Formal Basis for the Heuristic Determination of Minimum Cost Paths in Graphs," IEEE Trans. on Systems Science and Cybernetics, Vol. SSC-4, No. 2, pp 100-107, (July 1968). In addition to spawning a large literature, A* is used in many applications. Among these, it is the foundation of automobile- and web-based systems for computing driving directions; it is the most widely-used path-finding algorithm in video games; and it is used for string-matching and parsing, two of the most fundamental functions in computing. |
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Use of context in optical character recognition High accuracy OCR cannot be achieved solely through the recognition of individual characters. The first use of context to improve OCR accuracy was reported in: Duda, R. O. and P. E. Hart, "Experiments in the Recognition of Hand-Printed Text: Part II-- Context Analysis," AFIPS Conf. Proc., Vol. 33, pp 1139-1149, (Thompson Book Company, Washington DC, 1968). We were able to reduce the individual-character error rate by a factor of ten, a result that encouraged others to pursue this line of research. |
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| The "Hough" transform The so-called Hough transform for finding lines and shapes in digital images is a widely used algorithm in image analysis. Hough's original transform had a fatal theoretical flaw that made it computationally unworkable. The transform universally used today was first described in: Duda, R. O. and P. E. Hart, "Use of the Hough Transformation to Detect Lines and Curves in Pictures," Comm. ACM, Vol. 15, pp 1-15 (January, 1972). If Richard Duda and I had recognized how important our new transform would become in computer vision we might have been tempted to name it after ourselves, rather than continuing to ascribe it to Mr. Hough. |
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PROSPECTOR The PROSPECTOR expert system for mineral exploration was the first expert system to prove through field test that it could solve an economically important problem. The widely-repeated headline "PROSPECTOR DISCOVERS A MINE", correct in spirit though not literally accurate, ignited interest in artificial intelligence as a serious commercial technology. PROSPECTOR was first described in: Hart, P. E., "Progress on a Computer-Based Consultant," Proc. International Joint Conference on Artificial Intelligence, Vol. 2, pp 831-841, (Tbilisi, USSR, September 1975). |
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The big headlines came from our report in Science magazine: Campbell, A. N., V. F. Hollister, R. O. Duda and P. E. Hart, "Recognition of a Hidden Mineral Deposit by an Artificial Intelligence Program", SCIENCE , Vol. 217, No. 4563, pp 927-929 (3 September 1982). |
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Autonomous information retrieval Modern computer systems increasingly try to guess users’ intentions and autonomously provide information that might help them achieve their purpose. One of the first examples of this was reported in: Hart, P. E., and J. Graham, "Query-Free Information Retrieval," Proc. Second Int. Conf. on Cooperative Info. Systems, pp 36-46 (Toronto, Canada, May 17-20, 1994). Ricoh Corporation has used this system for over 10 years to support the operators in its national help desk center. |
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| Pattern Classification and Scene Analysis Duda, R. O. and P. E. Hart, Pattern Classification and Scene Analysis, (John Wiley & Sons, New York, NY, 1973). This graduate text is listed by Citeseer <http://citeseer.ist.psu.edu/articles.html > as the 9th most-cited reference in the field of Computer Science. It went through 19 printings over more than 25 years before being supplanted by the second edition, Pattern Classification (2nd ed, 2000) by R. O. Duda, P. E. Hart & D. G. Stork (Wiley), now in its 10th printing. |
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