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- Dana Angluin, Philip D. Laird
- Machine Learning
- 1987

- Philip D. Laird
- AAAI
- 1992

- Philip D. Laird
- AAAI
- 1986

- Philip D. Laird, Evan Gamble
- AAAI
- 1990

We show that the familiar explanation-based generalization (EBG) procedure is applicable to a large family of programming languages, including three families of importance to AI: logic programming (such as Pro-log); lambda calculus (such as LISP); and combinator languages (such as FP). The main application of this result is to extend the algorithm to… (More)

- Philip D. Laird, Ronald Saul
- International Conference on Evolutionary…
- 1994

- Philip D. Laird
- COLT
- 1988

- Philip D. Laird, Ronald Saul, Peter Dunning
- COLT
- 1993

We study sequence extrapolation as an abstract learning problem. The task is to learn a stream—a semi-infinite sequence of values all of the same data type-from a finite initial segment (sl, S2,. . .,s~), We assume that all elements of the stream are of the same type (e.g., integers, strings, etc.). In order to represent the hypotheses, we define a language… (More)

- Philip D. Laird, Evan Gamble
- Machine Learning
- 1991

- Philip D. Laird
- Commun. ACM
- 1979

In [2], Hanani presents an algorithm to optimize the evaluation of Boolean expressions for each record of a large file. The principal idea is that the operands of the Boolean functions A (AND) and V (OR) can be evaluated in any order because of the commutativity and associa-tivity of the operators; an optimal order, therefore, is one which minimizes the… (More)