Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Z. Alexin, T. Gyim6thy, and H. Bostr6m. IMPUT: An interactive learning tool based on program specialization. Intelligent Data Analysis, 1(4), 1997. {http://www-east.elsevier. com/ida/).
	2	Dana Angluin, Queries and Concept Learning, Machine Learning, v.2 n.4, p.319-342, April 1988  [doi>10.1023/A:1022821128753]
	3	Dana Angluin , Michael Frazier , Leonard Pitt, Learning Conjunctions of Horn Clauses, Machine Learning, v.9 n.2-3, p.147-164, July 1992  [doi>10.1007/BF00992675]
	4	Dana Angluin , Lisa Hellerstein , Marek Karpinski, Learning read-once formulas with queries, Journal of the ACM (JACM), v.40 n.1, p.185-210, Jan. 1993  [doi>10.1145/138027.138061]
	5	S. Argamon-Engelson and M. Koppel. Tractability of theory patching. Journal of Artificial Intelligence Research, 8:39-65, 1998.
	6	Avrim Blum , Lisa Hellerstein , Nick Littlestone, Learning in the presence of finitely or infinitely many irrelevant attributes, Journal of Computer and System Sciences, v.50 n.1, p.32-40, Feb. 1995  [doi>10.1006/jcss.1995.1004]
	7	Nader Bshouty , Lisa Hellerstein, Attribute-efficient learning in query and mistake-bound models, Journal of Computer and System Sciences, v.56 n.3, p.310-319, June 1, 1998  [doi>10.1006/jcss.1998.1571]
	8	John Case , Susanne Kaufmann , Efim Kinber , Martin Kummer, Learning recursive functions from approximations, Journal of Computer and System Sciences, v.55 n.1, p.183-196, Aug. 1997  [doi>10.1006/jcss.1997.1508]
	9	R. Davis , W. Hamscher, Model-based reasoning: troubleshooting, Exploring artificial intelligence, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1988
	10	Thomas Eiter , Georg Gottlob, On the complexity of propositional knowledge base revision, updates, and counterfactuals, Artificial Intelligence, v.57 n.2-3, p.227-270, Oct. 1992  [doi>10.1016/0004-3702(92)90018-S]
	11	P. G'~denfors. Knowledge in Flux. Bradford Books/MIT Press, Detroit/Cambridge, Mass., 1988.
	12	David P. Helmbold , Philip M. Long, Tracking Drifting Concepts By Minimizing Disagreements, Machine Learning, v.14 n.1, p.27-45, Jan. 1994  [doi>10.1023/A:1022694620923]
	13	Sanjay Jain , Arun Sharma, Learning in the presence of partial explanations, Information and Computation, v.95 n.2, p.162-191, Dec. 1991  [doi>10.1016/0890-5401(91)90043-2]
	14	Zvi Kohavi , Richard W. Hamming , Edward A. Feigenbaum, Switching and Finite Automata Theory: Computer Science Series, McGraw-Hill Higher Education, 1990
	15	M. Koppel, R. Feldman, and A. M. Segre. Bias-driven revision of logical domain theories. Journal of Artificial Intelligence Research, 1:159-208, 1994.
	16	John E. Laird , Allen Newell , Paul S. Rosenbloom, SOAR: an architecture for general intelligence, Artificial Intelligence, v.33 n.1, p.1-64, Sept. 1987  [doi>10.1016/0004-3702(87)90050-6]
	17	Wolfgang Maass , György Turán, Lower Bound Methods and Separation Results for On-Line Learning Models, Machine Learning, v.9 n.2-3, p.107-145, July 1992  [doi>10.1007/BF00992674]
	18	R. A. Marcotte, M. J. Neiberg, R. L. Piazza, and L. J. Holtzblatt. Model-based diagnostic reasoning using VHDL. In J. M. Schoen, editor, Performance and Fault Modeling with VttDL, chapter 6, pages 304-399. Prentice Hall, Englewood Cliffs, NJ, 1992.
	19	Johan de Kleer , Alan K. Mackworth , Raymond Reiter, Characterizing diagnoses and systems, Artificial Intelligence, v.56 n.2-3, p.197-222, Aug. 1992  [doi>10.1016/0004-3702(92)90027-U]
	20	R.J. Mooney. A preliminary PAC analysis of theory revision. In Computational Learning Theory and Natural Learning Systems, Volume 111: Selecting Good Models, chapter 3, pages 43-53. MIT Press, 1995.
	21	Dirk Ourston , Raymond J. Mooney, Theory refinement combining analytical and empirical methods, Artificial Intelligence, v.66 n.2, p.273-309, April 1994  [doi>10.1016/0004-3702(94)90028-0]
	22	R Reiter, A theory of diagnosis from first principles, Artificial Intelligence, v.32 n.1, p.57-95, April 1987  [doi>10.1016/0004-3702(87)90062-2]
	23	Bradley L. Richards , Raymond J. Mooney, Automated Refinement of First-Order Horn-Clause Domain Theories, Machine Learning, v.19 n.2, p.95-131, May 1995  [doi>10.1023/A:1022611224557]
	24	Ronald L. Rivest , Robert Sloan, A formal model of hierarchical concept learning, Information and Computation, v.114 n.1, p.88-114, Oct. 1994  [doi>10.1006/inco.1994.1081]
	25	Ehud Y. Shapiro, Algorithmic Program DeBugging, MIT Press, Cambridge, MA, 1983
	26	Geoffrey G. Towell , Jude W. Shavlik, Extracting Refined Rules from Knowledge-Based Neural Networks, Machine Learning, v.13 n.1, p.71-101, Oct. 1993  [doi>10.1023/A:1022683529158]
	27	Ingo Wegener, The complexity of Boolean functions, John Wiley & Sons, Inc., New York, NY, 1987
	28	Stefan Wrobel, Concept Formation During Interactive Theory Revision, Machine Learning, v.14 n.2, p.169-191, Feb. 1994  [doi>10.1023/A:1022674116380]
	29	S. Wrobel. First order theory refinement. In L. De Raedt, editor, Advances in ILP, pages 14-33. IOS Press, Amsterdam, 1995.

• Data structures for quadtree approximation and compression Communications of the ACM   28, 9
  Hanan Samet
  
  
• A hierarchical single-key-lock access control using the Chinese remainder theorem Proceedings of the 1992 ACM/SIGAPP Symposium on Applied computing
  Kim S. Lee ,  Huizhu Lu ,  D. D. Fisher
  
  
• Putting innovation to work: adoption strategies for multimedia communication systems Communications of the ACM   34, 12
  Ellen Francik ,  Susan Ehrlich Rudman ,  Donna Cooper ,  Stephen Levine
  
  
• The GemStone object database management system Communications of the ACM   34, 10
  Paul Butterworth ,  Allen Otis ,  Jacob Stein
  
  
• An intelligent component database for behavioral synthesis Proceedings of the 27th ACM/IEEE Design Automation Conference on
  Gwo-Dong Chen ,  Daniel D. Gajski