Abstract
The tension between deduction and induction is perhaps the most fundamental issue in areas such as philosophy, cognition and artificial intelligence (AI). The deduction camp concerns itself with questions about the expressiveness of formal languages for capturing knowledge about the world, together with proof systems for reasoning from such knowledge bases. The learning camp attempts to generalize from examples about partial descriptions about the world. In AI, historically, these camps have loosely divided the development of the field, but advances in cross-over areas such as statistical relational learning, neuro-symbolic systems, and high-level control have illustrated that the dichotomy is not very constructive, and perhaps even ill-formed.
In this article, we survey work that provides further evidence for the connections between logic and learning. Our narrative is structured in terms of three strands: logic versus learning, machine learning for logic, and logic for machine learning, but naturally, there is considerable overlap. We place an emphasis on the following “sore” point: there is a common misconception that logic is for discrete properties, whereas probability theory and machine learning, more generally, is for continuous properties. We report on results that challenge this view on the limitations of logic, and expose the role that logic can play for learning in infinite domains.
The author was supported by a Royal Society University Research Fellowship. He is grateful to Ionela G. Mocanu, Paulius Dilkas and Kwabena Nuamah for their feedback.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Albarghouthi, A., D’Antoni, L., Drews, S., Nori, A.V.: Quantifying program bias. CoRR, abs/1702.05437 (2017)
Bach, F.R., Jordan, M.I.: Thin junction trees. In: Advances in Neural Information Processing Systems, pp. 569–576 (2002)
Banihashemi, B., De Giacomo, G., Lespérance, Y.: Abstraction in situation calculus action theories. In: AAAI, pp. 1048–1055 (2017)
Barrett, C., Sebastiani, R., Seshia, S.A., Tinelli, C.: Satisfiability modulo theories. In: Handbook of Satisfiability, chap. 26, pp. 825–885. IOS Press (2009)
Belle, V.: Logic meets probability: towards explainable AI systems for uncertain worlds. In: IJCAI (2017)
Belle, V.: Open-universe weighted model counting. In: AAAI, pp. 3701–3708 (2017)
Belle, V.: Weighted model counting with function symbols. In: UAI (2017)
Belle, V.: Abstracting probabilistic models: relations, constraints and beyond. Knowl.-Based Syst. 199, 105976 (2020). https://www.sciencedirect.com/science/article/abs/pii/S0950705120302914
Belle, V., De Raedt, L.: Semiring programming: a declarative framework for generalized sum product problems. In: AAAI Workshop: Statistical Relational Artificial Intelligence (2020)
Belle, V., Juba, B.: Implicitly learning to reason in first-order logic. In: Advances in Neural Information Processing Systems, pp. 3376–3386 (2019)
Belle, V., Levesque, H.J.: Allegro: belief-based programming in stochastic dynamical domains. In: IJCAI (2015)
Belle, V., Passerini, A., Van den Broeck, G.: Probabilistic inference in hybrid domains by weighted model integration. In: IJCAI, pp. 2770–2776 (2015)
Benedikt, M., Kersting, K., Kolaitis, P.G., Neider, D.: Logic and learning (dagstuhl seminar 19361). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik (2020)
Bistarelli, S., Montanari, U., Rossi, F.: Semiring-based constraint logic programming: syntax and semantics. TOPLAS 23(1), 1–29 (2001)
Bueff, A., Speichert, S., Belle, V.: Tractable querying and learning in hybrid domains via sum-product networks. In: KR Workshop on Hybrid Reasoning (2018)
Bundy, A., Nuamah, K., Lucas, C.: Automated reasoning in the age of the internet. In: Fleuriot, J., Wang, D., Calmet, J. (eds.) AISC 2018. LNCS (LNAI), vol. 11110, pp. 3–18. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99957-9_1
Bunel, R., Hausknecht, M., Devlin, J., Singh, R., Kohli, P.: Leveraging grammar and reinforcement learning for neural program synthesis. ar**v preprint ar**v:1805.04276 (2018)
Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr., E.R., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: AAAI, pp. 1306–1313 (2010)
Chakraborty, S., Fremont, D.J., Meel, K.S., Seshia, S.A., Vardi, M.Y.: Distribution-aware sampling and weighted model counting for SAT. In: AAAI, pp. 1722–1730 (2014)
Chavira, M., Darwiche, A.: On probabilistic inference by weighted model counting. Artific. Intell. 172(6–7), 772–799 (2008)
Chistikov, D., Dimitrova, R., Majumdar, R.: Approximate counting in SMT and value estimation for probabilistic programs. TACAS 9035, 320–334 (2015)
Cohen, W.W.: PAC-learning nondeterminate clauses. In: AAAI, pp. 676–681 (1994)
Darwiche, A.: New advances in compiling CNF to decomposable negation normal form. In: ECAI, pp. 328–332 (2004)
Darwiche, A.: Three modern roles for logic in AI. In: Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, pp. 229–243 (2020)
Darwiche, A., Marquis, P.: A knowledge compilation map. J. Artif. Intell. Res. 17, 229–264 (2002)
De Raedt, L., Dries, A., Thon, I., Van den Broeck, G., Verbeke, M.: Inducing probabilistic relational rules from probabilistic examples. In: Twenty-Fourth International Joint Conference on Artificial Intelligence (2015)
De Raedt, L., Kimmig, A.: Probabilistic (logic) programming concepts. Mach. Learn. 100(1), 5–47 (2015)
De Raedt, L., Manhaeve, R., Dumancic, S., Demeester, T., Kimmig, A.: Neuro-symbolic= neural+ logical+ probabilistic. In: NeSy 2019@ IJCAI, The 14th International Workshop on Neural-Symbolic Learning and Reasoning, pp. 1–4 (2019)
Dilkas, P., Belle, V.: Generating random logic programs using constraint programming. CoRR, abs/2006.01889 (2020)
Domingos, P.: The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books (2015)
Dos Martires, P.Z., Dries, A., De Raedt, L.: Exact and approximate weighted model integration with probability density functions using knowledge compilation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 7825–7833 (2019)
Dries, A., Kimmig, A., Davis, J., Belle, V., De Raedt, L.: Solving probability problems in natural language. In: IJCAI (2017)
Eisner, J., Filardo, N.W.: Dyna: extending datalog for modern AI. In: de Moor, O., Gottlob, G., Furche, T., Sellers, A. (eds.) Datalog 2.0 2010. LNCS, vol. 6702, pp. 181–220. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24206-9_11
Ensan, A., Ternovska, E.: Modular systems with preferences. In: IJCAI, pp. 2940–2947 (2015)
Evans, R., Grefenstette, E.: Learning explanatory rules from noisy data. J. Artif. Intell. Res. 61, 1–64 (2018)
Fierens, D., Van den Broeck, G., Thon, I., Gutmann, B., De Raedt, L.: Inference in probabilistic logic programs using weighted CNF’s. In: UAI, pp. 211–220 (2011)
d’Avila Garcez, A., Gori, M., Lamb, L.C., Serafini, L., Spranger, M., Tran, S.N.: Neural-symbolic computing: an effective methodology for principled integration of machine learning and reasoning. ar**v preprint ar**v:1905.06088 (2019)
Getoor, L., Taskar, B. (eds.): An Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)
Gomes, C.P., Sabharwal, A., Selman, B.: Model counting. In: Handbook of Satisfiability. IOS Press (2009)
Goodman, N.D., Mansinghka, V.K., Roy, D.M., Bonawitz, K., Tenenbaum, J.B.: Church: a language for generative models. In: Proceedings of UAI, pp. 220–229 (2008)
Grohe, M., Lindner, P.: Probabilistic databases with an infinite open-world assumption. In: Proceedings of the 38th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, pp. 17–31 (2019)
Grohe, M., Ritzert, M.: Learning first-order definable concepts over structures of small degree. In: 2017 32nd Annual ACM/IEEE Symposium on Logic in Computer Science (LICS), pp. 1–12. IEEE (2017)
Gulwani, S.: Dimensions in program synthesis. In: PPDP, pp. 13–24. ACM (2010)
Gunning, D.: Explainable artificial intelligence (XAI). Technical report, DARPA/I20 (2016)
Gutmann, B., Thon, I., Kimmig, A., Bruynooghe, M., De Raedt, L.: The magic of logical inference in probabilistic programming. Theor. Pract. Logic Program. 11(4–5), 663–680 (2011)
Halpern, J.Y.: Reasoning about Uncertainty. MIT Press (2003)
Holtzen, S., Millstein, T.: and G. Van den Broeck. Probabilistic program abstractions, In UAI (2017)
Holtzen, S., Van den Broeck, G., Millstein, T.: Dice: compiling discrete probabilistic programs for scalable inference. ar**v preprint ar**v:2005.09089 (2020)
Huang, X., Kwiatkowska, M., Wang, S., Wu, M.: Safety verification of deep neural networks. In: Majumdar, R., Kunčak, V. (eds.) CAV 2017. LNCS, vol. 10426, pp. 3–29. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-63387-9_1
Kaelbling, L.P., Lozano-Pérez, T.: Integrated task and motion planning in belief space. I. J. Robotic Res. 32(9–10), 1194–1227 (2013)
Kahneman, D.: Thinking, Fast and Slow. Macmillan (2011)
Kimmig, A., Van den Broeck, G., De Raedt, L.: Algebraic model counting. J. Appl. Log. 22, 46–62 (2017)
Kolb, S., Mladenov, M., Sanner, S., Belle, V., Kersting, K.: Efficient symbolic integration for probabilistic inference. In: IJCAI (2018)
Kolb, S., et al.: The PYWMI framework and toolbox for probabilistic inference using weighted model integration (2019). https://www.ijcai.org/proceedings/2019/
Kolb, S., Teso, S., Passerini, A., De Raedt, L.: Learning SMT (LRA) constraints using SMT solvers. In: IJCAI, pp. 2333–2340 (2018)
Koller, D., Friedman, N.: Probabilistic Graphical Models - Principles and Techniques. MIT Press (2009)
Koller, D., Levy, A., Pfeffer, A.: P-classic: a tractable probablistic description logic. In: Proceedings of the AAAI/IAAI, pp. 390–397 (1997)
Kordjamshidi, P., Roth, D., Kersting, K.: Systems AI: a declarative learning based programming perspective. In: IJCAI, pp. 5464–5471 (2018)
Lakemeyer, G., Levesque, H.J.: Cognitive robotics. In: Handbook of Knowledge Representation, pp. 869–886. Elsevier (2007)
Lamb, L., Garcez, A., Gori, M., Prates, M., Avelar, P., Vardi, M.: Graph neural networks meet neural-symbolic computing: a survey and perspective. ar**v preprint ar**v:2003.00330 (2020)
Levesque, H.J.: Common Sense, the Turing Test, and the Quest for Real AI. MIT Press (2017)
Levesque, H.J., Brachman, R.J.: Expressiveness and tractability in knowledge representation and reasoning. Comput. Intell. 3, 78–93 (1987)
Liang, Y., Bekker, J., Van den Broeck, G.: Learning the structure of probabilistic sentential decision diagrams. In: Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI) (2017)
Lierler, Y., Truszczynski, M.: An abstract view on modularity in knowledge representation. In: AAAI, pp. 1532–1538 (2015)
Liu, Y., Levesque, H.: Tractable reasoning with incomplete first-order knowledge in dynamic systems with context-dependent actions. In: Proceedings of the IJCAI, pp. 522–527 (2005)
Lowd, D., Domingos, P.: Learning arithmetic circuits. In: Proceedings of the 24th Conference in Uncertainty in Artificial Intelligence (UAI), pp. 383–392 (2008)
Manhaeve, R., Dumancic, S., Kimmig, A., Demeester, T., De Raedt, L.: Deepproblog: neural probabilistic logic programming. In: Advances in Neural Information Processing Systems, pp. 3749–3759 (2018)
Marcus, G., Davis, E.: Rebooting AI: Building Artificial Intelligence We Can Trust. Pantheon (2019)
Merrell, D., Albarghouthi, A., D’Antoni, L.: Weighted model integration with orthogonal transformations. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (2017)
Milch, B., Marthi, B., Sontag, D., Russell, S.J., Ong, D.L., Kolobov, A.: Approximate inference for infinite contingent Bayesian networks. In: AISTATS, pp. 238–245 (2005)
Mitchell, D.G., Ternovska, E.: A framework for representing and solving NP search problems. In: AAAI, pp. 430–435 (2005)
Mocanu, I.G., Belle, V., Juba, B.: Polynomial-time implicit learnability in SMT. In: ECAI (2020)
Molina, A., Vergari, A., Di Mauro, N., Natarajan, S., Esposito, F., Kersting, K.: Mixed sum-product networks: a deep architecture for hybrid domains. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Morettin, P., Passerini, A., Sebastiani, R.: Advanced SMT techniques for weighted model integration. Artif. Intell. 275, 1–27 (2019)
Muggleton, S., De Raedt, L.: Inductive logic programming: theory and methods. J. Logic Program. 19, 629–679 (1994)
Nitti, D., Belle, V., De Laet, T., De Raedt, L.: Planning in hybrid relational mdps. Mach. Learn. 106(12), 1905–1932 (2017)
Nitti, D., Ravkic, I., Davis, J., Raedt, L.D.: Learning the structure of dynamic hybrid relational models. In: Proceedings of the Twenty-second European Conference on Artificial Intelligence, pp. 1283–1290. IOS Press (2016)
Niu, F., Ré, C., Doan, A., Shavlik, J.: Tuffy: scaling up statistical inference in markov logic networks using an rdbms. Proc. VLDB Endowment 4(6), 373–384 (2011)
Niu, F., Zhang, C., Ré, C., Shavlik, J.W.: Deepdive: web-scale knowledge-base construction using statistical learning and inference. VLDS 12, 25–28 (2012)
Papantonis, I., Belle, V.: On constraint definability in tractable probabilistic models. ar**v preprint ar**v:2001.11349 (2020)
Poole, D.: First-order probabilistic inference. In: Proceedings of the IJCAI, pp. 985–991 (2003)
Poon, H., Domingos, P.: Sum-product networks: a new deep architecture. In: UAI, pp. 337–346 (2011)
Raedt, L.D., Kersting, K., Natarajan, S., Poole, D.: Statistical relational artificial intelligence: logic, probability, and computation. Synth. Lect. Artif. Intell. Mach. Learn. 10(2), 1–189 (2016)
Renkens, J., et al.: ProbLog2: from probabilistic programming to statistical relational learning. In: Roy, D., Mansinghka, V., Goodman, N. (eds.) Proceedings of the NIPS Probabilistic Programming Workshop, December 2012. Accepted
Richardson, M., Domingos, P.: Markov logic networks. Mach. Learn. 62(1), 107–136 (2006)
Rudin, C., Ustun, B.: Optimized scoring systems: toward trust in machine learning for healthcare and criminal justice. Interfaces 48(5), 449–466 (2018)
Russell, S.J.: Unifying logic and probability. Commun. ACM 58(7), 88–97 (2015)
Sanner, S., Abbasnejad, E.: Symbolic variable elimination for discrete and continuous graphical models. In: AAAI (2012)
Shenoy, P., West, J.: Inference in hybrid Bayesian networks using mixtures of polynomials. Int. J. Approximate Reasoning 52(5), 641–657 (2011)
Singla, P., Domingos, P.M.: Markov logic in infinite domains. In: UAI, pp. 368–375 (2007)
Speichert, S., Belle, V.: Learning probabilistic logic programs in continuous domains. In: ILP (2019)
Sreedharan, S., Srivastava, S., Kambhampati, S.: Hierarchical expertise level modeling for user specific contrastive explanations. In: IJCAI, pp. 4829–4836 (2018)
Suciu, D., Olteanu, D., Ré, C., Koch, C.: Probabilistic databases. Synth. Lect. Data Manage. 3(2), 1–180 (2011)
Valiant, L.G.: Robust logics. Artif. Intell. 117(2), 231–253 (2000)
Van den Broeck, G.: Lifted Inference and Learning in Statistical Relational Models. Ph.D. thesis. KU Leuven (2013)
Xu, J., Zhang, Z., Friedman, T., Liang, Y., Van den Broeck, G.: A semantic loss function for deep learning with symbolic knowledge. In: International Conference on Machine Learning, pp. 5502–5511 (2018)
Xu, K., Li, J., Zhang, M., Du, S.S., Kawarabayashi, K.-I., Jegelka, S.: What can neural networks reason about? ar**v preprint ar**v:1905.13211 (2019)
Zellers, R., Bisk, Y., Schwartz, R., Choi, Y.: Swag: a large-scale adversarial dataset for grounded commonsense inference. ar**v preprint ar**v:1808.05326 (2018)
Zeng, Z., Van den Broeck, G.: Efficient search-based weighted model integration. ar**v preprint ar**v:1903.05334 (2019)
Zuidberg Dos Martires, P., Dries, A., De Raedt, L.: Knowledge compilation with continuous random variables and its application in hybrid probabilistic logic programming. ar**v preprint ar**v:1807.00614 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Belle, V. (2020). Symbolic Logic Meets Machine Learning: A Brief Survey in Infinite Domains. In: Davis, J., Tabia, K. (eds) Scalable Uncertainty Management. SUM 2020. Lecture Notes in Computer Science(), vol 12322. Springer, Cham. https://doi.org/10.1007/978-3-030-58449-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-58449-8_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58448-1
Online ISBN: 978-3-030-58449-8
eBook Packages: Computer ScienceComputer Science (R0)