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Heuristic search as evidential reasoning
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PRODIGY: An integrated architecture for planning and learning
Carbonell, J. G. | Knoblock, C. A. | Minton, S.
Technical report CMU-CS-89-189, Computer Science Department, Carnegie-Mellon University
The million pound bridge program
In Levy, D. and Beal, D. (Eds.), Heuristic Programming in Artificial Intelligence. Ellis Horwood.
Engineering approach to building complete, intelligent beings
Proc. SPIE - The International Society for Optical Engineering, 1002, 618-625.
Learnability and the Vapnik-Chervonenkis dimension
Blumer, A. | Ehrenfeucht, A. | Haussler, D. | Warmuth, M.
Valiant’s learnability model is extended to learning classes of concepts defined by regions in Euclidean space E”. The methods in this paper lead to a unified treatment of some of Valiant’s results, along with previous results on distribution-free convergence of certain pattern recognition algorithms. It is shown that the essential condition for distribution-free learnability is finiteness of the Vapnik-Chervonenkis dimension, a simple combinatorial parameter of the class of concepts to be learned. Using this parameter, the complexity and closure properties of learnable classes are analyzed, and the necessary and sufftcient conditions are provided for feasible learnability.JACM, 36 (4), 929-65
Divide and conquer under global constraints: A solution to the N-queens problem
J. Parallel and Distributed Computing, 6 (3), 649-62.
Discovering Structure from Motion in Monkey, Man and Machine
Using a parallel processing model,the current work explores how the biological visual system might solve this problem and how the neurophysiologist might go about understanding the solution.
Generalization of Back propagation to Recurrent and Higher Order Neural Networks
Fernando J. Pineda Applied Physics Laboratory, Johns Hopkins University Johns Hopkins Rd., Laurel MD 20707 Abstract A general method for deriving backpropagation algorithms for networks with recurrent and higher order networks is introduced. The propagation of activation in these networks is determined by dissipative differential equations. The error signal is backpropagated by integrating an associated differential equation. The method is introduced by applying it to the recurrent generalization of the feedforward backpropagation network. The method is extended to the case of higher order networks and to a constrained dynamical system for training a content addressable memory. The essential feature of the adaptive algorithms is that adaptive equation has a simple outer product form.