×

Robust logics. (English) Zbl 1346.68165

Vitter, Jeffrey Scott (ed.) et al., Proceedings of the 31st annual ACM symposium on theory of computing, STOC 1999. Atlanta, GA, USA, May 1–4, 1999. New York, NY: ACM, Association for Computing Machinery (ISBN 1-58113-067-8). 642-651 (1999).

MSC:

68T05 Learning and adaptive systems in artificial intelligence
68T27 Logic in artificial intelligence
Full Text: DOI

References:

[1] Aristotle. Prior Analytics. Book II, Part 23.
[2] A. Blum, et al. A polynomial time algorithm for learn  ing noisy linear threshold functions. In Proc. 37th IEEE \(yrap. on Theory of Computing, pages 330-338, 1996\)
[3] T. Bylander. Learning linear threshold functions in the presence of classification noise. In Proc. 7th A UM Conference on Computational Learning Theory, pages 340- 347, 1994. 10.1145/180139.181176
[4] R. Carnap. Logical Foundations of Probability. University of Chicago Press, Chicago, 1950. · Zbl 0040.07001
[5] E. Cohen. Learning noisy perceptrons by a perceptron in polynomial time. In Proc 38th IEEE Syrup. on Foundation of Computer Science, pages 514-523, 1997.
[6] W.W. Cohen and C.D. Page. Polynomial learnability and inductive logic programming’ Methods and results. New Generation Computing, 13(314):369-409, 1995.
[7] M.L. Ginsberg. Readings in Nonmonotonic Reasoning. Morgan Kaufmann, Los Altos, CA, 1989.
[8] A.R. Golding and D. Roth. Applying Winnow to context-sensitive spelling correction. In Proc 13th Int. Conf. on Machine Learning, pages 182-190, San Francisco, CA, 1996. Morgan Kanfmann.
[9] J.Y. Halpern. An analysis of first-order logics of probability. In Artificial Intelligence Journal, volume 46, pages 311-350, 1990. 10.1016/0004-3702(90)90019-V · Zbl 0723.03007
[10] D. Haussler. Quantifying inductive bias: AI learning algorithms and Valiant’s learning framework. Artificial Intelligence, 36:177-221, 1988. 10.1016/0004-3702(88)90002-1 · Zbl 0651.68104
[11] R. Khardon. Learning to take actions. In Proc. National Conference oN A ris’ficial Intelligence, pages 787- 792. AAAI, 1996.
[12] R. Khardon and D. Roth. Learning to reason with a restricted view. In Proc. 8th A CM Conference on Computational Learning Theory, pages 301-310, i995. 10.1145/225298.225335 · Zbl 0948.68093
[13] R. Khardon and D. Roth. Learning to reason. J. A CM, 44(5):697-72, 1997. 10.1145/265910.265918 · Zbl 0891.68112
[14] J-U. Kietz and S. Dzeroski. Inductive logic programming and learnability. SIGART Bulletin, 5(1):22-32, 1994. 10.1145/181668.181674
[15] J. Kivinen and M.K. Waxmuth. The perceptron algorithm vs. Winnow: linear vs. logarithmic mistake bounds when few input variables are relevant. In Proc. 8th A CM Conference on Computational Learning Theory, pages 289-296, 1995. 10.1145/225298.225333
[16] N. Litttestone. Learning quickly when irrelevant attributes abound: a new linear-threshold algorithm. Machine Learning, 2:285-318, 1988. 10.1023/A:1022869011914
[17] S. Muggleton and L. De Raedt. Inductive logic programming: Theory and methods. J. o/Logic Programming, 19:629-679, 1994. · Zbl 0816.68043
[18] J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, Los Altos, CA, 1988. · Zbl 0746.68089
[19] D. Roth. Learning to reason: the non-monotonic case. Proc. Int. Joint Conf. Art. Intl., pages 1178-118, 1995.
[20] D. Roth. A connectionist framework for reasoning: Reasoning with examples. In Proc. National Conference on Artificial Intelligence, pages 1256-1261. AAAI, 1996.
[21] D. Schuurmans and R. Greiner. Learning default concepts. Proc. I Oth Canadian Conference on Artificial Intelligence, \(CCI-96\), pages 99-I06, 1994.
[22] b.C. Valiant. A theory of the learnable. Comm. of ACM, 27(11):1134-1142, 1984. 10.1145/1968.1972 · Zbl 0587.68077
[23] L.G. Valiant. Circuits of the Mind. Oxford University · Zbl 0839.68078
[24] L.G. Valiant. Rationality. In Proc. 8th Ann. Conference on Uomputational Learning Theory, pages 3-14. ACM Press, 1995. 10.1145/225298.225299
[25] L.G. Valiant. A neuroidal architecture for cognitive computation. Lecture Notes in Computer Science, 1443:642-669, 1998. Springer Verlag, Also as a technical report: ftp://ftp.das.harvaxd.edu/techreports/tr- 1998.html.
[26] L.G. Valiant. Projection learning. In Proc. l lth A UM Syrup. on Computational Learning Theory, pages 287- 293, Madison, WI, 1998. Also, Machine Learning, To appear. 10.1145/279943.279999
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.