AI Magazine 6(4):40-62

Addison-Wesley

Co-winner of The 2005 AAAI Classic Paper Awards. Summary of Significance by Hector Levesque. Proc. AAAI-86

IEEE Expert, 1[#3]:23-29

"This paper describes a general method for estimating the nominal relationship and expected error (covariance) between coordinate frames representing the relative locations of ob jects. The frames may be known only indirectly through a series of spatial relationships, each with its associated error, arising from diverse causes, including positioning errors, measurement errors, or tolerances in part dimensions. This estimation method can be used to answer such questions as whether a camera attached to a robot is likely to have a particular reference object in its field of view. The calculated estimates agree well with those from an independent Monte Carlo simulation. The method makes it possible to decide in advance whether an uncertain relationship is known accu rately enough for some task and, if not, how much of an improvement in locational knowledge a proposed sensor will provide. The method presented can be generalized to six degrees offreedom and provides a practical means of esti mating the relationships ( position and orientation) among objects, as well as estimating the uncertainty associated with the relationships." Int. J. Robotics Research, 5 (4), 56-68.

"Chunking was first proposed as a model of human memory by Miller (1956), and has since become a major component of theories of cognition. More recently it has been proposed that a theory of human learning based on chunking ..." Kluwer Academic Publishers, Norwell, MA, USA.

Computational Linguistics 12(3):175-204

Machine Learning, 1, 11–46.

The technology for building knowledge-based systems by inductive inference from examples hasbeen demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directionsMachine Learning, 1, p. 81-106

Also see the . New York: Springer Verlag, (2nd edition 1986, 3rd edition 1990, 4th edition 1995)