Abstract
Heuristics such as the Occam Razor’s principle have played a significant role in reducing the search for solutions of a learning task, by giving preference to most compressed hypotheses. For some application domains, however, these heuristics may become too weak and lead to solutions that are irrelevant or inapplicable. This is particularly the case when hypotheses ought to conform, within the scope of a given language bias, to precise domain-dependent structures. In this paper we introduce a notion of inductive learning through constraint-driven bias that addresses this problem. Specifically, we propose a notion of learning task in which the hypothesis space, induced by its mode declaration, is further constrained by domain-specific denials, and acceptable hypotheses are (brave inductive) solutions that conform with the given domain-specific constraints. We provide an implementation of this new learning task by extending the ASPAL learning approach and leveraging on its meta-level representation of hypothesis space to compute acceptable hypotheses. We demonstrate the usefulness of this new notion of learning by applying it to two class of problems - automated revision of software system goals models and learning of stratified normal programs.
Keywords
This research is partially funded by the 7th Framework EU-FET project 600792 “ALLOW Ensembles”, and the EPSRC project EP/K033522/1 “Privacy Dynamics”.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Throughout this paper we use \(\vDash \) to refer to brave consequence.
- 2.
We do not present here details of the mapping from temporal logic to logic programs as this is outside the scope of this paper.
- 3.
The complete program is available from http://www.doc.ic.ac.uk/~da04/ilp14/goalmodel.lp.
- 4.
Clingo’s option –project was used when running the ASP programs for solving the examples using the constraints to ensure that each hypothesis is output only once regardless of the number of ways it could be stratified.
- 5.
All tasks were run using the ASP solver Clingo 3 [13] on a 2.13 GHz laptop computer with 4 GB memory.
References
Alrajeh, D., Kramer, J., van Lamsweerde, A., Russo, A., Uchitel, S.: Generating obstacle conditions for requirements completeness. In: ICSE, pp. 705–715 (2012)
Alrajeh, D., Kramer, J., Russo, A., Uchitel, S.: Elaborating requirements using model checking and inductive learning. IEEE Trans. SE 39(3), 361–383 (2013)
Apt, K.R., Blair, H.A., Walker, A.: Foundations of deductive databases and logic programming. In: Minker, J. (ed.) Towards a Theory of Declarative Knowledge, pp. 89–148. Morgan Kaufmann, Los Altos (1988)
Blockeel, H., Raedt, L.D.: Top-down induction of first-order logical decision trees. Artif. Intell. 101(1–2), 285–297 (1998)
Bragaglia, S., Ray, O.: Nonmonotonic learning in large biological networks. In: 24th International Conference on Inductive Logic Programming (2014)
Chan, D.: Constructive negation based on the completed database. In: Proceedings of the Fifth International Conference and Symposium on Logic Programming, 1988, (2 Volumes), pp. 111–125 (1988)
Christiansen, H.: Executable specifications for hypothesis-based reasoning with prolog and constraint handling rules. J. Appl. Log. 7(3), 341–362 (2009)
Corapi, D.: Nonmonotonic inductive logic programming as abductive search. Ph.D. thesis, Imperial College London (2011)
Corapi, D., Russo, A., Lupu, E.: Inductive logic programming as abductive search. In: Hermenegildo, M.V., Schaub, T. (eds.) ICLP (Technical Communications). LIPIcs, vol. 7, pp. 54–63 (2010)
Corapi, D., Russo, A., Lupu, E.: Inductive logic programming in answer set programming. In: Muggleton, S.H., Tamaddoni-Nezhad, A., Lisi, F.A. (eds.) ILP 2011. LNCS, vol. 7207, pp. 91–97. Springer, Heidelberg (2012)
Darimont, R., van Lamsweerde, A.: Formal refinement patterns for goal-driven requirements elaboration. In: FSE, pp. 179–190. ACM (1996)
Eiter, T., Faber, W., Leone, N., Pfeifer, G.: Computing preferred answer sets by meta-interpretation in answer set programming. TPLP 3(4), 463–498 (2003)
Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T.: Clingo = ASP + control: preliminary report. In: ICLP 2014, vol. 14(4–5) (2014)
Inoue, K.: Induction as consequence finding. ML 55(2), 109–135 (2004)
Inoue, K., Doncescu, A., Nabeshima, H.: Completing causal networks by meta-level abduction. Mach. Learn. 91(2), 239–277 (2013)
Jorge, A., Brazdil, P.: Integrity constraints in ILP using a Monte Carlo approach. In: Muggleton, S. (ed.) ILP 1996. LNCS, vol. 1314, pp. 137–151. Springer, Heidelberg (1996)
Kakas, A., Michael, A., Mourlas, C.: ACLP: abductive constraint logic programming (2000)
Kakas, A.C., Kowalski, R.A., Toni, F.: Abductive logic programming. J. Log. Comput. 2(6), 719–770 (1992)
Kakas, A.C., Nuffelen, B.V., Denecker, M.: A-system: problem solving through abduction. In: Nebel, B. (ed.) Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, 2001, pp. 591–596. Morgan Kaufmann (2001)
Kowalski, R.A., Sergot, M.: A logic-based calculus of events. New Gener. Comput. 4(1), 67–95 (1986)
van Lamsweerde, A., Letier, E.: Handling obstacles in goal-oriented requirements engineering. IEEE Trans. SE 26(10), 978–1005 (2000)
Lin, D., Dechter, E., Ellis, K., Tenenbaum, J.B., Muggleton, S.: Bias reformulation for one-shot function induction. In: ECAI 2014. Frontiers in Artificial Intelligence and Applications, vol. 263, pp. 525–530 (2014)
Manna, Z., Pnueli, A.: The Temporal Logic of Reactive and Concurrent Systems. Springer, New York (1992)
Sakama, C., Inoue, K.: Brave induction: a logical framework for learning from incomplete information. Mach. Learn. 76(1), 3–35 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Athakravi, D., Alrajeh, D., Broda, K., Russo, A., Satoh, K. (2015). Inductive Learning Using Constraint-Driven Bias. In: Davis, J., Ramon, J. (eds) Inductive Logic Programming. Lecture Notes in Computer Science(), vol 9046. Springer, Cham. https://doi.org/10.1007/978-3-319-23708-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-23708-4_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23707-7
Online ISBN: 978-3-319-23708-4
eBook Packages: Computer ScienceComputer Science (R0)