Learning to reason with a restricted view

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Learning to reason with a restricted view

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Annual Workshop on Computational Learning Theory archive
Proceedings of the eighth annual conference on Computational learning theory table of contents

Santa Cruz, California, United States

Pages: 301 - 310

Year of Publication: 1995

ISBN:0-89791-723-5

Authors

Roni Khardon	Aiken Computation Laboratory, Harvard University, Cambridge, MA
Dan Roth	Aiken Computation Laboratory, Harvard University, Cambridge, MA

Sponsors

SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
University of California : University of California

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 15, Downloads (12 Months): 23, Citation Count: 8

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REFERENCES

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.

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	Blu92	Avrim Blum, Learning Boolean Functions in an Infinite Attribute Space, Machine Learning, v.9 n.4, p.373-386, Oct. 1992 [doi>10.1023/A:1022653502461]
	Bsh93	N.H. Bshouty. Exact learning via the monotone theory. In Proceedings of the IEEE Sym'p. on Foundation of Computer Science, pages 302- 311, Palo Alto, CA., 1993.
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	FP93	M. Frazier and L. Pitt. Learning from entailment: An application to propositional Horn sentences. In Proceedings of the International Conference on Machine Learning. Morgan Kaufmann, 1993.
	GS92	R. Greiner and D. Schuurmans. Learning useful Horn approximations. In Proceedings of the Internatwnal Conference on the Principles of Knowledge Representation and Reasoning, pages 383-392, 1992.
	HGR94	T. Hancock, R. Greiner, and R. B. Rao. Exploiting the absence of irrelevant information. In AAAI Fall Symposium on Relevance, pages 178- 183, 1994.
	Kir91	David Kirsh, Foundations of AI: the big issues, Artificial Intelligence, v.47 n.1-3, p.3-30, Jan. 1991 [doi>10.1016/0004-3702(91)90048-O]
	KR94a	Roni Khardon , Dan Roth, Learning to reason, Proceedings of the twelfth national conference on Artificial intelligence (vol. 1), p.682-687, October 1994, Seattle, Washington, United States
	KR94b	Roni Khardon , Dan Roth, Reasoning with models, Proceedings of the twelfth national conference on Artificial intelligence (vol. 2), p.1148-1153, October 1994, Seattle, Washington, United States
	KR95	Roni Khardon , Dan Roth, Reasoning with models, Proceedings of the twelfth national conference on Artificial intelligence (vol. 2), p.1148-1153, October 1994, Seattle, Washington, United States
	KS91	Henry A. Kautz , Bart Selman, Hard problems for simple default logics, Artificial Intelligence, v.49 n.1-3, p.243-279, May 1991 [doi>10.1016/0004-3702(91)90011-8]
	LC94	D.D. Lewis and J. Catlett. Heterogeneous uncertainty sampling for supervised learning. In Proceedings of the Eleventh International Workshop on Machine Learning, pages 148-156, 1994.
	Pap91	Christos H. Papadimitriou, On selecting a satisfying truth assignment (extended abstract), Proceedings of the 32nd annual symposium on Foundations of computer science, p.163-169, September 1991, San Juan, Puerto Rico [doi>10.1109/SFCS.1991.185365]
	Rei87	R. Reiter. Nonmonotonic reasoning. In Annual Reviews of Computer Science, pages 147-188. 1987.
	Rot93	Dan Roth, On the hardness of approximate reasoning, Artificial Intelligence, v.82 n.1-2, p.273-302, April 1996 [doi>10.1016/0004-3702(94)00092-1]
	Rot95	D. Roth. Learning to reason with incomplete information. In Proceedings of the International Joint Conference of Artificial Intelligence, August 1995.
	Sel90	B. ~elman. Tractable Default R ..... inst. PhD thesis, Department of Computer Science, University of Toronto, 1990.
	SG94	D. Schuurmans and R. Greiner. Learning default concepts. In Proceedings of the Tenth Canadian Conference on Artificial Intelhgence (CSCSI-9J), 1994.
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	Val94	L.G. Valiant. Rationality. Technical Report TR- 32-94, Aiken Computation Lab., Harvard University, November 1994.

CITED BY 8

	Roni Khardon , Dan Roth, Learning to reason, Journal of the ACM (JACM), v.44 n.5, p.697-725, Sept. 1997
	Leslie G. Valiant, Rationality, Proceedings of the eighth annual conference on Computational learning theory, p.3-14, July 05-08, 1995, Santa Cruz, California, United States
	Leslie G. Valiant, Robust logics, Proceedings of the thirty-first annual ACM symposium on Theory of computing, p.642-651, May 01-04, 1999, Atlanta, Georgia, United States
	Stephen S. Kwek, An apprentice learning model (extended abstract), Proceedings of the twelfth annual conference on Computational learning theory, p.63-74, July 07-09, 1999, Santa Cruz, California, United States
	Roni Khardon, Learning to Take Actions, Machine Learning, v.35 n.1, p.57-90, April 1999
	Russell Greiner , Adam J. Grove , Dan Roth, Learning cost-sensitive active classifiers, Artificial Intelligence, v.139 n.2, p.137-174, August 2002
	Roni Khardon , Dan Roth, Default-reasoning with models, Proceedings of the 14th international joint conference on Artificial intelligence, p.319-325, August 20-25, 1995, Montreal, Quebec, Canada
	Dan Roth, Learning to reason the non monotonic case, Proceedings of the 14th international joint conference on Artificial intelligence, p.1178-1184, August 20-25, 1995, Montreal, Quebec, Canada