Abstract
In science, experiments are empirical observations allowing for the arbitration of competing hypotheses and knowledge acquisition. For a scientist that aims at learning an agent strategy, performing experiments involves costs. To that extent, the efficiency of a learning process relies on the number of experiments performed. We study in this article how the cost of experimentation can be reduced with active learning to learn efficient agent strategies. We consider an extension of the meta-interpretive learning framework that allocates a Bayesian posterior distribution over the hypothesis space. At each iteration, the learner queries the label of the instance with maximum entropy. This produces the maximal discriminative over the remaining competing hypotheses, and thus achieves the highest shrinkage of the version space. We study the theoretical framework and evaluate the gain on the cost of experimentation for the task of learning regular grammars and agent strategies: our results demonstrate the number of experiments to perform to reach an arbitrary accuracy level can at least be halved.
The original version of this chapter was revised: The authors affiliation was corrected. The correction to this chapter is available at https://doi.org/10.1007/978-3-319-99960-9_11
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18 November 2018
Due to an internal error during the production process, the wrong affiliation of an author was entered in the originally published article. This was corrected.
Notes
- 1.
Code for these experiments available at https://github.com/celinehocquette/Bayesian-MIL-active-learning.git.
References
Angluin, D.: Queries and concept learning. J. Autom. Reason. 2(4), 319–42 (1988)
Angluin, D.: Queries revisited. Theor. Comput. Sci. 313(2), 175–194 (2004)
Bryant, C.H., Muggleton, S.H., Oliver, S.G., Kell, D.B., Reiser, P., King, R.D.: Combining inductive logic programming, active learning and robotics to discover the function of genes. Electron. Trans. Artif. Intell. 5–B1(012), pp. 1–36 (2001)
Cohn, D., Atlas, L., Ladner, R.: Improving generalization with active learning. Mach. Learn. 15(2), 201–221 (1994)
Cover, M.T., Thomas, J.A.: Elements of Information Theory. Wiley (2006)
Cropper, A., Muggleton, S.H.: Learning efficient logical robot strategies involving composable objects. In: IJCAI 2015, pp. 3423–3429 (2015)
Cropper, A., Muggleton, S.H.: Logical minimisation of meta-rules within meta-interpretive learning. In: Davis, J., Ramon, J. (eds.) ILP 2014. LNCS (LNAI), vol. 9046, pp. 62–75. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23708-4_5
Cropper, A., Muggleton, S.H.: Learning efficient logic programs. Mach. Learn. (2018)
Cropper, A., Muggleton, S.H.: Learning higher-order logic programs through abstraction and invention. In: IJCAI 2016, pp. 1418–1424 (2016)
Dasgupta, S.: Analysis of a greedy active learning strategy. Adv. Neural Inf. Process. Syst. 17, 337–344 (2005)
Dasgupta, S.: Coarse sample complexity bounds for active learning. In: Proceedings of the 18th International Conference on Neural Information Processing Systems, pp. 235–242 (2005)
Freund, Y., Seung, H.S., Shamir, E., Tishby, N.: Selective samping using the query by committee algorithm. Mach. Learn., pp. 1551–1557 (1997)
Hanneke, S.: A bound on the label complexity of agnostic active learning. In: ICML (2007)
Hanneke, S.: Theory of disagreement-based active learning. Found. Trends Mach. Learn. 7 (2014)
Haussler, D., Kearns, M., Schapire, R.E.: Bounds on the sample complexity of bayesian learning using information theory and the VC dimension. Mach. Learn. 14, 83–113 (1994)
von Frisch, K.: The dance language and orientation of bees. The Belknap Press of Harvard University Press, Cambridge, Massachussets (1967)
King, R.D., et al.: Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427, 247–252 (2004)
Kulkarni, S.R., Mitter, S.K., Tsitsiklis, J.N.: Active learning using arbitrary binary valued queries. Mach. Learn. 11, 23–35 (1993)
Lang, T., Toussaint, M., Kersting, K.: Exploration in relational worlds. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS (LNAI), vol. 6322, pp. 178–194. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15883-4_12
Lewis, D., Gale, W.: A sequential algorithm for training text classifiers. In: Croft, B.W., van Rijsbergen, C.J. (eds.) SIGIR 1994, pp. 3–12. ACM/Springer, London (1994). https://doi.org/10.1007/978-1-4471-2099-5_1
Mitchell, T.M.: Version Spaces: An Approach to Concept Learning. PhD Thesis (1978)
Muggleton, S.H., Lin, D.: Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited. In: Proceedings of the 23rd International Joint Conference Artificial Intelligence, pp. 1551–1557 (2013)
Muggleton, S.H., Lin, D., Chen, J., Tamaddoni-Nezhad, A.: MetaBayes: bayesian meta-interpretative learning using higher-order stochastic refinement. In: Zaverucha, G., Santos Costa, V., Paes, A. (eds.) ILP 2013. LNCS (LNAI), vol. 8812, pp. 1–17. Springer, Heidelberg (2014)
Muggleton, S.H., Lin, D., Pahlavi, N., Tamaddoni-Nezhad, A.: Meta-interpretive learning: application to grammatical inference. Mach. Learn. 94, 25–49 (2014)
Rodrigues, C., Gérard, P., Rouveirol, C., Soldano, H.: Active learning of relational action models. In: Muggleton, S.H., Tamaddoni-Nezhad, A., Lisi, F.A. (eds.) ILP 2011. LNCS (LNAI), vol. 7207, pp. 302–316. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31951-8_26
Roy, S., Namboodiri, V.P., Biswas, A.: Active learning with version spaces for object detection. ArXiv e-prints, November 2016
Settles, B.: Active learning literature survey. 52, July 2010
Thompson, C.A., Califf, M.E., Mooney, R.J.: Active learning for natural language parsing and information extraction. In: Proceedings of the 16th International Conference on Machine Learning, ICML 1999, pp. 406–414. Morgan Kaufmann Publishers Inc. (1999)
Tosh, C., Dasgupta, S.: Diameter-based active learning. CoRR, abs/1702.08553 (2017)
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Hocquette, C., Muggleton, S. (2018). How Much Can Experimental Cost Be Reduced in Active Learning of Agent Strategies?. In: Riguzzi, F., Bellodi, E., Zese, R. (eds) Inductive Logic Programming. ILP 2018. Lecture Notes in Computer Science(), vol 11105. Springer, Cham. https://doi.org/10.1007/978-3-319-99960-9_3
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