Abstract
Existing work on Inductive Logic Programming (ILP) has focused mainly on the learning of definite programs or normal logic programs. In this paper, we aim to push the computational boundary to a wider class of programs: Answer Set Programs. We propose a new paradigm for ILP that integrates existing notions of brave and cautious semantics within a unifying learning framework whose inductive solutions are Answer Set Programs and examples are partial interpretations We present an algorithm that is sound and complete with respect to our new notion of inductive solutions. We demonstrate its applicability by discussing a prototype implementation, called ILASP (Inductive Learning of Answer Set Programs), and evaluate its use in the context of planning. In particular, we show how ILASP can be used to learn agent’s knowledge about the environment. Solutions of the learned ASP program provide plans for the agent to travel through the given environment.
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References
Corapi, D., Russo, A., Lupu, E.: Inductive logic programming as abductive search. In: ICLP (Technical Communications), 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)
De Raedt, L.: Logical settings for concept-learning. Artificial Intelligence 95(1), 187–201 (1997)
Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T., Schneider, M.: Potassco: The Potsdam answer set solving collection. AI Communications 24(2), 107–124 (2011)
Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T.: Answer Set Solving in Practice. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan and Claypool Publishers (2012)
Gelfond, M., Lifschitz, V.: The stable model semantics for logic programming. In: ICLP/SLP, vol. 88, pp. 1070–1080 (1988)
Inoue, K., Ribeiro, T., Sakama, C.: Learning from interpretation transition. Machine Learning 94(1), 51–79 (2014)
Kimber, T., Broda, K., Russo, A.: Induction on failure: learning connected horn theories. In: Erdem, E., Lin, F., Schaub, T. (eds.) LPNMR 2009. LNCS, vol. 5753, pp. 169–181. Springer, Heidelberg (2009)
Law, M., Russo, A., Broda, K.: Proofs for inductive learning of answer set programs, https://www.doc.ic.ac.uk/~ml1909/ILASP_Proofs.pdf
Muggleton, S.: Inductive logic programming. New Generation Computing 8(4), 295–318 (1991)
Muggleton, S., De Raedt, L., Poole, D., Bratko, I., Flach, P., Inoue, K., Srinivasan, A.: Ilp turns 20. Machine Learning 86(1), 3–23 (2012)
Muggleton, S., Lin, D.: Meta-interpretive learning of higher-order dyadic datalog: Predicate invention revisited. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, pp. 1551–1557. AAAI Press (2013)
Otero, R.: Induction of stable models. In: Rouveirol, C., Sebag, M. (eds.) ILP 2001. LNCS (LNAI), vol. 2157, pp. 193–205. Springer, Heidelberg (2001)
Ray, O.: Nonmonotonic abductive inductive learning. Journal of Applied Logic 7(3), 329–340 (2009)
Ray, O., Broda, K., Russo, A.: A hybrid abductive inductive proof procedure. Logic Journal of IGPL 12(5), 371–397 (2004)
Sakama, C., Inoue, K.: Brave induction: a logical framework for learning from incomplete information. Machine Learning 76(1), 3–35 (2009)
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Law, M., Russo, A., Broda, K. (2014). Inductive Learning of Answer Set Programs. In: Fermé, E., Leite, J. (eds) Logics in Artificial Intelligence. JELIA 2014. Lecture Notes in Computer Science(), vol 8761. Springer, Cham. https://doi.org/10.1007/978-3-319-11558-0_22
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DOI: https://doi.org/10.1007/978-3-319-11558-0_22
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