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.
This research is partially funded by the 7th Framework EU-FET project 600792 “ALLOW Ensembles”, and the EPSRC project EP/K033522/1 “Privacy Dynamics”.
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Notes
- 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.
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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
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