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- 1
 - 
Thai Bui.
Solving the 8-puzzle with genetic programming.
In John R. Koza, editor, Genetic Algorithms and Genetic
  Programming at Stanford, pages 11-17. Stanford Bookstore, Stanford,
  California, 1997.
 - 2
 - 
W. W. Cohen.
Using distribution-free learning theory to analyze solution path
  caching mechanisms.
Computational Intelligence, 8(2):336-375, 1992.
 - 3
 - 
Oren Etzioni and Ruth Etzioni.
Statistical methods for analyzing speedup learning experiments.
Machine Learning, 14:333-347, 1994.
 - 4
 - 
R. E. Fikes, P. E. Hart, and N. J. Nilsson.
Learning and executing generalized robot plans.
Artificial Intelligence, 3:251-288, 1972.
 - 5
 - 
Lev Finkelshtein and Shaul Markovitch.
Selective acquisition and utilization of macro operators: A
  learning program solves the general N X N puzzle.
Technical report CIS9216, Computer Science Department, Technion,
  Haifa, Israel, 1992.
 - 6
 - 
M. L. Ginsberg and W. D. Harvey.
Iterative broadening.
Artificial Intelligence, 55(2-3):367-383, June 1992.
 - 7
 - 
J. Gratch and D. DeJong.
COMPOSER: A probabilistic solution to the utility problem in
  speed-up learning.
In Proceedings of the Tenth National Conference on Artificial
  Intelligence, pages 235-240, San Jose, California, 1992. Morgan Kaufmann.
 - 8
 - 
Russell Greiner and Joseph Likuski.
Incorporating redundant learned rules: A preliminary formal
  analysis of EBL.
In Proceedings of the Eleventh International Joint Conference on
  Artificial Intelligence, pages 744-749, Detroit, Michigan, 1989. Morgan
  Kaufmann.
 - 9
 - 
G. A. Iba.
Learning by discovering macros in puzzle solving.
In Proceedings of the Ninth International Joint Conference on
  Artificial Intelligence, pages 640-642, Los Angeles, California, 1985.
  Morgan Kaufmann.
 - 10
 - 
G. A. Iba.
A heuristic approach to the discovery of macro-operators.
Machine Learning, 3(4):285-317, 1989.
 - 11
 - 
Wayne Iba, James Wogulis, and Pat Langley.
Trading off simplicity and coverage in incremental concept learning.
In Proceedings of the Fifth International Conference on Machine
  Learning, pages 73-79, Ann Arbor, MI, 1988. Morgan Kaufmann.
 - 12
 - 
W. W. Johnson and W. E. Story.
Notes on the ``15'' puzzle.
American Journal of Mathematics, 2:397-404, 1879.
 - 13
 - 
R. E. Korf.
Learning to Solve Problems by Searching for Macro-Operators.
Pitman, Boston, 1985.
 - 14
 - 
R. E. Korf.
Linear-space best-first search.
Artificial Intelligence, 62(1):41-78, July 1993.
 - 15
 - 
Richard E. Korf.
Depth-first iterative-deepening: An optimal admissible tree search.
Artificial Intelligence, 27(1):97-109, 1985.
 - 16
 - 
Richard E. Korf.
Real-time search for dynamic planning.
In Proceedings of the AAAI Spring Symposium on Planning in
  Uncertain, Unpredictable, or Changing Environments, pages 72-76, Stanford,
  CA, March 1990.
 - 17
 - 
Richard E. Korf and Larry A. Taylor.
Finding optimal solutions to the twenty-four puzzle.
In Proceedings of the Fourteenth National Conference on
  Artificial Intelligence, pages 1202-1207, Portland, Oregon, 1996. Morgan
  Kaufmann.
 - 18
 - 
Maurice Kraitchik.
Mathematical Recreations.
Dover, New York, 1953.
 - 19
 - 
J. E. Laird, P. S. Rosenbloom, and A. Newell.
Chunking in soar: The anatomy of a general learning mechanism.
Machine Learning, 1:11-46, 1986.
 - 20
 - 
Shaul Markovitch and Irit Rosdeutscher.
Systematic experimentation with deductive learning: Satisficing vs.
  optimizing search.
In Proceedings of the Knowledge Compilation and Speedup Learning
  Workshop, Aberdeen, Scotland, 1992.
 - 21
 - 
Shaul Markovitch and Paul D. Scott.
The role of forgetting in learning.
In Proceedings of The Fifth International Conference on Machine
  Learning, pages 459-465, Ann Arbor, MI, 1988. Morgan Kaufmann.
 - 22
 - 
Shaul Markovitch and Paul D. Scott.
Information filtering: Selection mechanisms in learning systems.
Machine Learning, 10(2):113-151, February 1993.
 - 23
 - 
S. Minton.
Selectively generalizing plans for problem solving.
In Proceedings of the Ninth International Joint Conference on
  Artificial Intelligence, pages 596-599, Los Angeles, CA, 1985. Morgan
  Kaufmann.
 - 24
 - 
S. Minton.
Learning Search Control Knowledge: An Explanation-Based
  Approach.
Kluwer, Boston, MA, 1988.
 - 25
 - 
Tom M. Mitchell, Paul E. Utgoff, and Ranan Banerji.
Learning by experimentation: Acquiring and refining problem-solving
  heuristics.
In R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, editors,
  Machine Learning: An Artificial Intelligence Approach. Tioga, Palo
  Alto, California, 1983.
 - 26
 - 
Raymond Mooney.
The effect of rule use on the utility of explanation-based learning.
In Proceedings of the Eleventh International Joint Conference of
  Artificial Intelligence, pages 725-730, Detroit, Michigan, 1989. Morgan
  Kaufmann.
 - 27
 - 
B. K. Natarajan.
On learning from exercises.
In Proc. 2nd Annu. Workshop on Comput. Learning Theory, pages
  72-87, San Mateo, CA, 1989. Morgan Kaufmann.
 - 28
 - 
Nils J. Nilsson.
Principles of Artificial Intelligence.
Tioga, Palo Alto, CA, 1980.
 - 29
 - 
Judea Pearl.
Heuristics: intelligent search strategies for computer problem
  solving.
Addison-Wesley, Reading, Massachusetts, 1984.
 - 30
 - 
D. Ratner and M. Warmuth.
Finding a shortest solution for the n  x  n extension of the
  15-puzzle is intractable.
In Proceedings of the Fifth National Conference on Artificial
  Intelligence (AAAI-86), volume 1, pages 168-172, Philadelphia,
  Pennsylvania, August 1986. Morgan Kaufmann.
 - 31
 - 
Daniel Ratner and Manfred Warmuth.
The (N2-1)-puzzle and related relocation problems.
Journal of Symbolic Computation, 10:111-137, 1990.
 - 32
 - 
Chandra Reddy and Prasad Tadepalli.
Learning goal-decomposition rules using exercises.
In Proceedings of the Fourteenth International Conference on
  Machine Learning, Nashville, TN, 1997. Morgan Kaufmann.
 - 33
 - 
David Ruby and Dennis Kibler.
Learning subgoal sequences for planning.
In Proceedings of the Eleventh International Joint Conference on
  Artificial Intelligence, pages 609-614, Detroit, Michigan, 1989. Morgan
  Kaufmann.
 - 34
 - 
David Ruby and Dennis Kibler.
EASe: Integrating search with learning episodes.
Technical Report 92-30, Information and Computer Science, University
  of California, Irvine, California, 1992.
 - 35
 - 
P. D. A. Schofield.
Complete solution of the eight-puzzle.
In N. L. Collins and D. Michie, editors, Machine Intelligence,
  volume 1. Oliver and Boyd, Edinburgh, 1967.
 - 36
 - 
Alberto Segre, Charles Elkan, and Alexander Russell.
A critical look at experimental evaluation of EBL.
Machine Learning, 6:183-195, 1991.
 - 37
 - 
Jude W. Shavlik.
Acquiring recursive and iterative concepts with explanation-based
  learning.
Machine Learning, 5:39-70, 1990.
 - 38
 - 
H. A. Simon and J. B. Kadane.
Optimal problem-solving search: All-or-none solution.
Artificial Intelligence, 6:235-247, 1975.
 - 39
 - 
Devika Subramanian and Scott Hunter.
Measuring utility and the design of provably good EBL algorithms.
In Proceedings of the Ninth International Workshop on Machine
  Learning, pages 426-435, Aberdeen, Scotland, 1992. Morgan Kaufmann.
 - 40
 - 
P. Tadepalli and B. K. Natarajan.
A formal framework for speedup learning from problems and solutions.
Journal of Artificial Intelligence Research, 4:419-443, 1996.
 - 41
 - 
Prasad Tadepalli.
A formalization of explanation-based macro-operator learning.
In Proceedings of International Joint Conference for Artificial
  Intelligence, pages 616-622, Sydney, Australia, 1991. Morgan Kaufmann.
 - 42
 - 
P. G. Tait.
Note on the theory of the ``15 puzzle''.
Proceedings of the Royal Society of Edinburgh, 10:664-665,
  1880.
 
Shaul Markovitch
1998-07-21