zbMATH — the first resource for mathematics

Learning effect axioms via probabilistic logic programming. (English) Zbl 1428.68286
Rocha, Ricardo (ed.) et al., Technical communications of the 33rd international conference on logic programming, ICLP 2017, August 28 – September 1, 2017, Melbourne, Australia. Wadern: Schloss Dagstuhl – Leibniz Zentrum für Informatik. OASIcs – OpenAccess Ser. Inform. 58, Article 8, 15 p. (2018).
Summary: In this paper we showed how we can automatically learn the structure and parameters of probabilistic effect axioms for the Simple Event Calculus (SEC) from positive and negative example interpretations stated as short dialogue sequences in natural language. We used the cplint framework for this task that provides libraries for structure and parameter learning and for answering queries with exact and inexact inference. The example dialogues that are used for learning the structure of the probabilistic logic program are parsed into dependency structures and then further translated into the Event Calculus notation with the help of a simple ontology. The novelty of our approach is that we can not only process uncertainty in event recognition but also learn the structure of effect axioms and combine these two sources of uncertainty to successfully answer queries under this probabilistic setting. Interestingly, our extension of the logic-based version of the SEC is completely elaboration-tolerant in the sense that the probabilistic version fully includes the logic-based version. This makes it possible to use the probabilistic version of the SEC in the traditional way as well as when we have to deal with uncertainty in the observed world. In the future, we would like to extend the probabilistic version of the SEC to deal – among other things – with concurrent actions and continuous change.
For the entire collection see [Zbl 1407.68043].
68T27 Logic in artificial intelligence
68N17 Logic programming
68T37 Reasoning under uncertainty in the context of artificial intelligence
Full Text: DOI
[2] Elena Bellodi and Fabrizio Riguzzi. Expectation Maximization over binary decision diagrams for probabilistic logic programs. In Intelligent Data Analysis, 17(2), pp. 343-363, 2013.
[3] Elena Bellodi and Fabrizio Riguzzi. Structure learning of probabilistic logic programs by searching the clause space. In Theory and Practice of Logic Programming, 15(2), pp. 169- 212, 2015. · Zbl 1379.68269
[4] Luc De Raedt, Angelika Kimmig, and Hannu Toivonen. ProbLog: A probabilistic Prolog and its application in link discovery. In Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI-07), Hyderabad, India, pp. 2462-2467, 2007.
[5] Luc De Raedt and Angelika Kimmig. Probabilistic (logic) programming concepts. In Ma- chine Learning, Vol. 100, Issue 1, Springer New York LLC, pp. 5-47, 2015. · Zbl 1346.68050
[6] Anton Dries, Angelika Kimmig, Wannes Meert, Joris Renkens, Guy Van den Broeck, Jonas Vlasselaer and Luc De Raedt. ProbLog2: Probabilistic logic programming. In Machine Learning and Knowledge Discovery in Databases, LNCS 9286, Springer, pp. 312-315, 2015.
[7] Nikos Katzouris, Alexander Artikis, and Georgios Paliouras. Incremental learning of event definitions with Inductive Logic Programming. In Machine Learning, Vol. 100, Issue 2, pp. 555-585, 2015. · Zbl 1341.68159
[8] :15
[9] :15
[10] Angelika Kimmig, Bart Demoen, Luc De Raedt, Vitor Santos Costa and Ricardo Rocha. On the implementation of the probabilistic logic programming language ProbLog. In: Theory and Practice of Logic Programming, Vol. 11, pp. 235-262, 2011. · Zbl 1220.68037
[11] Robert Kowalski and Marek Sergot. A Logic-Based Calculus of Events. In New Generation Computing, Vol. 4, pp. 67-95, 1986. · Zbl 1356.68221
[12] John W. Lloyd. Foundations of logic programming. Second, Extended Edition. SpringerVerlag, New York, 1987.
[13] Christopher, D. Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. The Stanford CoreNLP Natural Language Processing Toolkit. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55-60, 2014.
[14] Rob Miller and Murray Shanahan. Some Alternative Formulations of the Event Calculus. In Computational Logic: Logic Programming and Beyond - Essays in Honour of Robert A. Kowaski, LNAI 2408, Springer pp. 452-490, 2002. · Zbl 1012.68192
[15] Erik T. Mueller. Commonsense Reasoning, An Event Calculus Based Approach. 2nd Edition, Morgan Kaufmann/Elesevier, 2015.
[16] David Poole. The independent choice logic for modelling multiple agents under uncertainty. In Artificial Intelligence Vol. 94, pp. 7-56, 1997. · Zbl 0902.03017
[17] David Poole. The independent choice logic and beyond. In L. De Raedt, P. Frasconi, K. Kersting, and S. Muggleton (eds.),Probabilistic Inductive Logic Programming: Theory and Application, LNAI Vol. 4911, Springer, pp. 222-243, 2008. · Zbl 1137.68596
[18] Matthew Richardson and Pedro Domingos. Markov logic networks. In Machine Learning, Vol. 62, Issue 1, pp. 107-136, 2006.
[19] Fabrizio Riguzzi and Terrance Swift. An extended semantics for logic programs with annotated disjunctions and its efficient implementation. In Italian Conference on Computational Logic. CEUR Workshop Proceedings, Vol. 598. Sun SITE Central Europe, 2010. · Zbl 1237.68049
[20] Fabrizio Riguzzi and Terrance Swift. The PITA system: Tabling and answer subsumption for reasoning under uncertainty. In Theory and Practice of Logic Programming, 27th Inter- national Conference on Logic Programming (ICLP’11) Special Issue, 11(4-5), pp. 433-449, 2011. · Zbl 1218.68169
[21] Fabrizio Riguzzi. MCINTYRE: A Monte Carlo system for probabilistic logic programming. In:Fundamenta Informaticae, 124(4), pp. 521-541, 2013.
[22] Fabrizio Riguzzi, Elena Bellodi, and Riccardo Zese. A History of Probabilistic Inductive Logic Programming. In Frontiers in Robotics and AI, 18. September 2014. · Zbl 1309.68027
[23] Fabrizio Riguzzi and Terrance Swift. Probabilistic logic programming under the distribution semantics. In M. Kifer and Y. A. Liu, (eds),Declarative Logic Programming: Theory, Systems, and Applications, LNCS. Springer, 2016. · Zbl 1267.68084
[24] Fabrizio Riguzzi. cplint Manual. SWI-Prolog Version. July 4, 2017.
[25] Taisuke Sato. A statistical learning method for logic programs with distribution semantics. In L. Stearling (ed.), 12th International Conference on Logic Programming, Cambridge: MIT Press, pp. 715-729, 1995.
[26] Murray Shanahan. The Event Calculus Explained. In M.J. Wooldridge and M. Veloso (eds), Artificial Intelligence Today, LNAI, Vol. 1600, Springer, pp. 409-430, 1999.
[27] Anastasios Skarlatidis, Alexander Artikis, Jason Filippou, Georgios Paliouras. A Probabilistic Logic Programming Event Calculus. In Theory and Practice of Logic Programming, Vol. 15, No. 2, pp. 213-245, 2015. · Zbl 1379.68305
[28] Anastasios Skarlatidis, Georgios Paliouras, Alexander Artikis, and George A. Vouros. Probabilistic Event Calculus for Event Recognition. In ACM Transactions on Computational Logic, Vol. 16, No. 2, Article 11, 2015. · Zbl 1354.68266
[29] Joost Vennekens, Sofie Verbaeten, and Maurice Bruynooghe. Logic programs with annotated disjunctions. In International Conference on Logic Programming LNCS 3131, Berlin: Springer, pp. 195-209, 2004. · Zbl 1104.68391
[30] JasonWeston.Dialog-basedLanguageLearning.FacebookAIResearch.In arXiv:1604.06045v7, 24th October 2016.
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. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.