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Towards a Reconciliation Between Reasoning and Learning - A Position Paper

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Scalable Uncertainty Management (SUM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11940))

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Abstract

The paper first examines the contours of artificial intelligence (AI) at its beginnings, more than sixty years ago, and points out the important place that machine learning already had at that time. The ambition of AI of making machines capable of performing any information processing task that the human mind can do, means that AI should cover the two modes of human thinking: the instinctive (reactive) one and the deliberative one. This also corresponds to the difference between mastering a skill without being able to articulate it and holding some pieces of knowledge that one can use to explain and teach. In case a function-based representation applies to a considered AI problem, the respective merits of learning a universal approximation of the function vs. a rule-based representation are discussed, with a view to better draw the contours of AI. Moreover, the paper reviews the relative positions of knowledge and data in reasoning and learning, and advocates the need for bridging the two tasks. The paper is also a plea for a unified view of the various facets of AI as a science.

A preliminary version of this paper was presented at the 2018 IJCAI-ECAI workshop “Learning and Reasoning: Principles & Applications to Everyday Spatial and Temporal Knowledge”, Stockholm, July 13–14.

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Notes

  1. 1.

    One would notice the word ‘logical’ in the title of this pioneering paper.

  2. 2.

    Still this function-based approach is often cast in a probabilistic modeling paradigm.

References

  1. Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) Proceedings 1993 ACM SIGMOD International Conference on Management of Data, Washington, DC, 26–28 May 1993, pp. 207–216. ACM Press (1993)

    Google Scholar 

  2. Amel, K.R.: From shallow to deep interactions between knowledge representation, reasoning and machine learning. In: BenAmor, N., Theobald, M. (eds.) Proceedings 13th International Conference Scala Uncertainity Mgmt (SUM 2019), Compiègne, LNCS, 16–18 December 2019. Springer, Heidelberg (2019)

    Google Scholar 

  3. Augustin, T., Coolen, F.P.A., De Cooman, G., Troffaes, M.C.M.: Introduction to Imprecise Probabilities. Wiley, Hoboken (2014)

    Book  MATH  Google Scholar 

  4. Baader, F., Horrocks, I., Lutz, C., Sattler, U.: An Introduction to Description Logic. Cambridge University Press, Cambridge (2017)

    Book  MATH  Google Scholar 

  5. Bach, S.H., Broecheler, M., Huang, B., Getoor, L.: Hinge-loss Markov random fields and probabilistic soft logic. J. Mach. Learn. Res. 18, 109:1–109:67 (2017)

    MathSciNet  MATH  Google Scholar 

  6. Bajcsy, R., Reynolds, C.W.: Computer science: the science of and about information and computation. Commun. ACM 45(3), 94–98 (2002)

    Article  Google Scholar 

  7. Balkenius, C., Gärdenfors, P.: Nonmonotonic inferences in neural networks. In: Proceedings 2nd International Conference on Principle of Knowledge Representation and Reasoning (KR 1991), Cambridge, MA, pp. 32–39 (1991)

    Google Scholar 

  8. Benferhat, S., Dubois, D., Prade, H.: Possibilistic and standard probabilistic semantics of conditional knowledge bases. J. Log. Comput. 9(6), 873–895 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  9. Benferhat, S., Dubois, D., Lagrue, S., Prade, H.: A big-stepped probability approach for discovering default rules. Int. J. Uncert. Fuzz. Knowl.-based Syst. 11(Suppl.–1), 1–14 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  10. Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)

    Article  Google Scholar 

  11. Besold, T.R., Garcez, A.D.A., Stenning, K., van der Torre, L., van Lambalgen, M.: Reasoning in non-probabilistic uncertainty: logic programming and neural-symbolic computing as examples. Minds Mach. 27(1), 37–77 (2017)

    Article  Google Scholar 

  12. Bichler, O., Querlioz, D., Thorpe, S.J., Bourgoin, J.-P., Gamrat, C.: Extraction of temporally correlated features from dynamic vision sensors with spike-timing-dependent plasticity. Neural Netw. 32, 339–348 (2012)

    Article  Google Scholar 

  13. Bounhas, M., Pirlot, M., Prade, H., Sobrie, O.: Comparison of analogy-based methods for predicting preferences. In: BenAmor, N., Theobald, M. (eds.) Proceedings 13th International Conference on Scala Uncertainity Mgmt (SUM 2019), Compiègne, LNCS, 16–18 December. Springer, Heidelberg (2019)

    Google Scholar 

  14. Bounhas, M., Prade, H., Richard, G.: Analogy-based classifiers for nominal or numerical data. Int. J. Approx. Reasoning 91, 36–55 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  15. Brabant, Q., Couceiro, M., Dubois, D., Prade, H., Rico, A.: Extracting decision rules from qualitative data via sugeno utility functionals. In: Medina, J., Ojeda-Aciego, M., Verdegay, J.L., Pelta, D.A., Cabrera, I.P., Bouchon-Meunier, B., Yager, R.R. (eds.) IPMU 2018. CCIS, vol. 853, pp. 253–265. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91473-2_22

    Chapter  Google Scholar 

  16. Cohen, W.W.: TensorLog: a differentiable deductive database. CoRR, abs/1605.06523 (2016)

    Google Scholar 

  17. Cohen, W.W., Yang, F., Mazaitis, K.: TensorLog: deep learning meets probabilistic DBs. CoRR, abs/1707.05390 (2017)

    Google Scholar 

  18. Couceiro, M., Hug, N., Prade, H., Richard, G.: Analogy-preserving functions: a way to extend Boolean samples. In: Proceedings 26th International Joint Conference on Artificial Intelligence, (IJCAI 2017), Melbourne, 19–25 August, pp. 1575–1581 (2017)

    Google Scholar 

  19. Couso, I., Dubois, D.: A general framework for maximizing likelihood under incomplete data. Int. J. Approx. Reasoning 93, 238–260 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  20. d’Alché-Buc, F., Andrés, V., Nadal, J.-P.: Rule extraction with fuzzy neural network. Int. J. Neural Syst. 5(1), 1–11 (1994)

    Article  Google Scholar 

  21. Darwiche, A.: Human-level intelligence or animal-like abilities?. CoRR, abs/1707.04327 (2017)

    Google Scholar 

  22. d’Avila Garcez, A.S., Broda, K., Gabbay, D.M.: Symbolic knowledge extraction from trained neural networks: a sound approach. Artif. Intell. 125(1–2), 155–207 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  23. d’Avila Garcez, A.S., Gabbay, D.M., Lamb, L.C.: Value-based argumentation frameworks as neural-symbolic learning systems. J. Logic Comput. 15(6), 1041–1058 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  24. d’Avila Garcez, A.S., Lamb, L.C., Gabbay, D.M.: Connectionist modal logic: representing modalities in neural networks. Theor. Comput. Sci. 371(1–2), 34–53 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  25. Donadello, I., Serafini, L., Garcez, A.D.A.: Logic tensor networks for semantic image interpretation. In: Sierra, C. (ed) Proceedings 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), Melbourne, 19–25 August 2017, pp. 1596–1602 (2017)

    Google Scholar 

  26. Dubois, D., Godo, L., Prade, H.: Weighted logics for artificial intelligence - an introductory discussion. Int. J. Approx. Reasoning 55(9), 1819–1829 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  27. Dubois, D., Prade, H.: Soft computing, fuzzy logic, and artificial intelligence. Soft Comput. 2(1), 7–11 (1998)

    Article  Google Scholar 

  28. Dubois, D., Prade, H., Richard, G.: Multiple-valued extensions of analogical proportions. Fuzzy Sets Syst. 292, 193–202 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  29. Dubois, D., Prade, H., Rico, A.: The logical encoding of Sugeno integrals. Fuzzy Sets Syst. 241, 61–75 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  30. Dubois, D., Prade, H., Schockaert, S.: Generalized possibilistic logic: foundations and applications to qualitative reasoning about uncertainty. Artif. Intell. 252, 139–174 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  31. Dupin de Saint-Cyr, F., Lang, J., Schiex, T.: Penalty logic and its link with Dempster-Shafer theory. In: de Mántaras, R.L., Poole, D. (eds.) Proceedings 10th Annual Conference on Uncertainty in Artificial Intelligence (UAI 1994), Seattle, 29–31 July, pp. 204–211 (1994)

    Google Scholar 

  32. Fahandar, M.A., Hüllermeier, E.: Learning to rank based on analogical reasoning. In: Proceedings 32th National Conference on Artificial Intelligence (AAAI 2018), New Orleans, 2–7 February 2018 (2018)

    Google Scholar 

  33. Fakhraei, S., Raschid, L., Getoor, L.: Drug-target interaction prediction for drug repurposing with probabilistic similarity logic. In: SIGKDD 12th International Workshop on Data Mining in Bioinformatics (BIOKDD). ACM (2013)

    Google Scholar 

  34. Farnadi, G., Bach, S.H., Moens, M.F., Getoor, L., De Cock, M.: Extending PSL with fuzzy quantifiers. In: Papers from the 2014 AAAI Workshop Statistical Relational Artificial Intelligence, Québec City, 27 July, pp. WS-14-13, 35–37 (2014)

    Google Scholar 

  35. Gilboa, I., Schmeidler, D.: Case-based decision theory. Q. J. Econ. 110, 605–639 (1995)

    Article  MATH  Google Scholar 

  36. Hájek, P., Havránek, T.: Mechanising Hypothesis Formation - Mathematical Foundations for a General Theory. Springer, Heidelberg (1978). https://doi.org/10.1007/978-3-642-66943-9

    Book  MATH  Google Scholar 

  37. Hebb, D.O.: The Organization of Behaviour. Wiley, Hoboken (1949)

    Google Scholar 

  38. Heitjan, D., Rubin, D.: Ignorability and coarse ckata. Ann. Statist. 19, 2244–2253 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  39. Hobbes, T.: Elements of philosophy, the first section, concerning body. In: Molesworth, W. (ed.) The English works of Thomas Hobbes of Malmesbury, vol. 1. John Bohn, London, 1839. English translation of "Elementa Philosophiae I. De Corpore" (1655)

    Google Scholar 

  40. Hohenecker, P., Lukasiewicz, T.: Ontology reasoning with deep neural networks. CoRR, abs/1808.07980 (2018)

    Google Scholar 

  41. Hüllermeier, E.: Inducing fuzzy concepts through extended version space learning. In: Bilgiç, T., De Baets, B., Kaynak, O. (eds.) IFSA 2003. LNCS, vol. 2715, pp. 677–684. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-44967-1_81

    Chapter  MATH  Google Scholar 

  42. Hüllermeier, E.: Learning from imprecise and fuzzy observations: data disambiguation through generalized loss minimization. Int. J. Approx. Reasoning 55(7), 1519–1534 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  43. Jaeger, M.: Ignorability in statistical and probabilistic inference. JAIR 24, 889–917 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  44. Kahneman, D.: Thinking, Fast and Slow. Farrar, Straus and Giroux, New York (2011)

    Google Scholar 

  45. Kolodner, J.L.: Case-Based Reasoning. Morgan Kaufmann, Burlington (1993)

    Book  MATH  Google Scholar 

  46. Kotlowski, W., Slowinski, R.: On nonparametric ordinal classification with monotonicity constraints. IEEE Trans. Knowl. Data Eng. 25(11), 2576–2589 (2013)

    Article  Google Scholar 

  47. Kraus, S., Lehmann, D., Magidor, M.: Nonmonotonic reasoning, preferential models and cumulative logics. Artif. Intell. 44, 167–207 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  48. Kuzelka, O., Davis, J., Schockaert, S.: Encoding Markov logic networks in possibilistic logic. In: Meila, M., Heskes, T. (eds.) Proceedings 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015), Amsterdam, 12–16 July 2015, pp. 454–463. AUAI Press (2015)

    Google Scholar 

  49. Kuzelka, O., Davis, J., Schockaert, S.: Learning possibilistic logic theories from default rules. In: Kambhampati, S. (ed.) Proceedings 25th International Joint Conference on Artificial Intelligence (IJCAI 2016), New York, 9–15 July 2016, pp. 1167–1173 (2016)

    Google Scholar 

  50. Kuzelka, O., Davis, J., Schockaert, S.: Induction of interpretable possibilistic logic theories from relational data. In: Sierra, C. (ed.) Proceedings 26th International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, 19–25 August 2017, pp. 1153–1159 (2017)

    Google Scholar 

  51. LeCun, Y., Bengio, Y., Hinton, G.E.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  52. Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man-Mach. Stu. 7, 1–13 (1975)

    Article  MATH  Google Scholar 

  53. Marquis, P., Papini, O., Prade, H.: Eléments pour une histoire de l’intelligence artificielle. In: Panorama de l’Intelligence Artificielle. Ses Bases Méthodologiques, ses Développements, vol. I, pp. 1–39. Cépaduès (2014)

    Google Scholar 

  54. Marquis, P., Papini, O., Prade, H.: Some elements for a prehistory of Artificial Intelligence in the last four centuries. In: Proceedings 21st Europoen Conference on Artificial Intelligence (ECAI 2014), Prague, pp. 609–614. IOS Press (2014)

    Google Scholar 

  55. McCarthy, J., Minsky, M., Roch-ester, N., Shannon, C.E.: A proposal for the Dartmouth summer research project on artificial intelligence, august 31, 1955. AI Mag. 27(4), 12–14 (2006)

    Google Scholar 

  56. McCulloch, W.S., Pitts, W.: A logical calculus of ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943)

    Article  MathSciNet  MATH  Google Scholar 

  57. Miclet, L., Bayoudh, S., Delhay, A.: Analogical dissimilarity: definition, algorithms and two experiments in machine learning. JAIR 32, 793–824 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  58. Miclet, L., Prade, H.: Handling analogical proportions in classical logic and fuzzy logics settings. In: Sossai, C., Chemello, G. (eds.) ECSQARU 2009. LNCS (LNAI), vol. 5590, pp. 638–650. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02906-6_55

    Chapter  MATH  Google Scholar 

  59. Mitchell, T.: Version spaces: an approach to concept learning. Ph.D. thesis, Stanford (1979)

    Google Scholar 

  60. More, T.: On the construction of Venn diagrams. J. Symb. Logic 24(4), 303–304 (1959)

    Article  MathSciNet  MATH  Google Scholar 

  61. Mushthofa, M., Schockaert, S., De Cock, M.: Solving disjunctive fuzzy answer set programs. In: Calimeri, F., Ianni, G., Truszczynski, M. (eds.) LPNMR 2015. LNCS (LNAI), vol. 9345, pp. 453–466. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23264-5_38

    Chapter  Google Scholar 

  62. Narodytska, N.: Formal analysis of deep binarized neural networks. In: Lang, J. (ed.) Proceedings 27th International Joint Conference Artificial Intelligence (IJCAI 2018), Stockholm, 13–19 July 2018, pp. 5692–5696 (2018)

    Google Scholar 

  63. Newell, A., Simon, H.A.: The logic theory machine. a complex information processing system. In: Proceedings IRE Transactions on Information Theory(IT-2), The Rand Corporation, Santa Monica, Ca, 1956. Report P-868, 15 June 1956, pp. 61-79, September 1956

    Article  Google Scholar 

  64. Nilsson, N.J.: The Quest for Artificial Intelligence : A History of Ideas andAchievements. Cambridge University Press, Cambridge (2010)

    Google Scholar 

  65. Nin, J., Laurent, A., Poncelet, P.: Speed up gradual rule mining from stream data! A B-tree and OWA-based approach. J. Intell. Inf. Syst. 35(3), 447–463 (2010)

    Article  Google Scholar 

  66. Pearl, J.: Causality, vol. 2000, 2nd edn. Cambridge University Press, Cambridge (2009)

    Book  MATH  Google Scholar 

  67. Perfilieva, I., Dubois, D., Prade, H., Esteva, F., Godo, L., Hodáková, P.: Interpolation of fuzzy data: analytical approach and overview. Fuzzy Sets Syst. 192, 134–158 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  68. Pinkas, G.: Propositional non-monotonic reasoning and inconsistency in symmetric neural networks. In: Mylopoulos, J., Reiter, R. (eds.) Proceedings 12th International Joint Conference on Artificial Intelligence, Sydney, 24–30 August 1991, pp. 525–531. Morgan Kaufmann (1991)

    Google Scholar 

  69. Pinkas, G.: Reasoning, nonmonotonicity and learning in connectionist networks that capture propositional knowledge. Artif. Intell. 77(2), 203–247 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  70. Pinkas, G., Cohen, S.: High-order networks that learn to satisfy logic constraints. FLAP J. Appl. Logics IfCoLoG J. Logics Appl. 6(4), 653–694 (2019)

    Google Scholar 

  71. Prade, H.: Reasoning with data - a new challenge for AI? In: Schockaert, S., Senellart, P. (eds.) SUM 2016. LNCS (LNAI), vol. 9858, pp. 274–288. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45856-4_19

    Chapter  Google Scholar 

  72. Prade, H., Richard, G.: From analogical proportion to logical proportions. Logica Universalis 7(4), 441–505 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  73. Prade, H., Rico, A., Serrurier, M.: Elicitation of sugeno integrals: a version space learning perspective. In: Rauch, J., Raś, Z.W., Berka, P., Elomaa, T. (eds.) ISMIS 2009. LNCS (LNAI), vol. 5722, pp. 392–401. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04125-9_42

    Chapter  Google Scholar 

  74. Prade, H., Rico, A., Serrurier, M., Raufaste, E.: Elicitating sugeno integrals: methodology and a case study. In: Sossai, C., Chemello, G. (eds.) ECSQARU 2009. LNCS (LNAI), vol. 5590, pp. 712–723. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02906-6_61

    Chapter  MATH  Google Scholar 

  75. Prade, H., Serrurier, M.: Bipolar version space learning. Int. J. Intell. Syst. 23, 1135–1152 (2008)

    Article  MATH  Google Scholar 

  76. Raufaste, E.: Les Mécanismes Cognitifs du Diagnostic Médical : Optimisation et Expertise. Presses Universitaires de France (PUF), Paris (2001)

    Google Scholar 

  77. Richardson, M., Domingos, P.M.: Markov logic networks. Mach. Learn. 62(1–2), 107–136 (2006)

    Article  Google Scholar 

  78. Rocktäschel, T., Riedel, S.: End-to-end differentiable proving. In: Guyon, I., et al. (eds.) Proceedings 31st Annual Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, 4–9 December 2017, pp. 3791–3803 (2017)

    Google Scholar 

  79. Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6), 386–408 (1958)

    Article  Google Scholar 

  80. Rückert, U., De Raedt, L.: An experimental evaluation of simplicity in rule learning. Artif. Intell. 172(1), 19–28 (2008)

    Article  MATH  Google Scholar 

  81. Samuel, A.: Some studies in machine learning using the game of checkers. IBM J. 3, 210–229 (1959)

    Article  MathSciNet  Google Scholar 

  82. Schockaert, S., Prade, H.: Interpolation and extrapolation in conceptual spaces: a case study in the music domain. In: Rudolph, S., Gutierrez, C. (eds.) RR 2011. LNCS, vol. 6902, pp. 217–231. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23580-1_16

    Chapter  Google Scholar 

  83. Schockaert, S., Prade, H.: Interpolative and extrapolative reasoning in propositional theories using qualitative knowledge about conceptual spaces. Artif. Intell. 202, 86–131 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  84. Selfridge, O.G.: Pandemonium: a paradigm for learning. In: Blake, D.V., Uttley, A.M. (ed) Symposium on Mechanisation of Thought Processes, London, 24–27 November 1959, vol. 1958, pp. 511–529 (1959)

    Google Scholar 

  85. Serafini, L., Garcez, A.S.A.: Logic tensor networks: deep learning and logical reasoning from data and knowledge. In: Besold, T.R., Lamb, L.C., Serafini, L., Tabor, W. (eds.) Proceedings 11th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy 2016), New York City, 16–17 July 2016, vol. 1768 of CEUR Workshop Proceedings (2016)

    Google Scholar 

  86. Serafini, L., Donadello, I., Garcez, A.S.A.: Learning and reasoning in logic tensor networks: theory and application to semantic image interpretation. In: Seffah, A., Penzenstadler, B., Alves, C., Peng, X. (eds.) Proceedings Symposium on Applied Computing (SAC 2017), Marrakech, 3–7 April 2017, pp. 125–130. ACM (2017)

    Google Scholar 

  87. Serrurier, M., Dubois, D., Prade, H., Sudkamp, T.: Learning fuzzy rules with their implication operators. Data Knowl. Eng. 60(1), 71–89 (2007)

    Article  Google Scholar 

  88. Serrurier, M., Prade, H.: Introducing possibilistic logic in ILP for dealing with exceptions. Artif. Intell. 171(16–17), 939–950 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  89. Shannon, C.E.: Programming a computer for playing chess. Philos. Mag. (7th series) XLI (314), 256–275 (1950)

    Google Scholar 

  90. Solomonoff, R.J.: An inductive inference machine. Tech. Res. Group, New York City (1956)

    Google Scholar 

  91. Turing, A.M.: Intelligent machinery. Technical report, National Physical Laboratory, London, 1948. Also. In: Machine Intelligence, vol. 5, pp. 3–23. Edinburgh University Press (1969)

    Google Scholar 

  92. Turing, A.M.: Computing machinery and intelligence. Mind 59, 433–460 (1950)

    Article  MathSciNet  Google Scholar 

  93. Ughetto, L., Dubois, D., Prade, H.: Implicative and conjunctive fuzzy rules - a tool for reasoning from knowledge and examples. In: Hendler, J., Subramanian, D. (eds.) Proceedings 16th National Confernce on Artificial Intelligence, Orlando, 18–22 July 1999, pp. 214–219 (1999)

    Google Scholar 

  94. Walley, P.: Statistical Reasoning with Imprecise Probabilities. Chapman and Hall, London (1991)

    Book  MATH  Google Scholar 

  95. Walley, P.: Measures of uncertainty in expert systems. Artif. Intell. 83(1), 1–58 (1996)

    Article  MathSciNet  Google Scholar 

  96. Wiener, N.: Cybernetics or Control and Communication in the Animal and the Machine. Wiley, Hoboken (1949)

    Book  Google Scholar 

  97. Zadeh, L.A.: Thinking machines - a new field in electrical engineering. Columbia Eng. Q. 3, 12–13 (1950)

    Google Scholar 

  98. Zadeh, L.A.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man Cybern. 3(1), 28–44 (1973)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

The authors thank Emiliano Lorini, Dominique Longin, Gilles Richard, Steven Schockaert, Mathieu Serrurier for useful exchanges on some of the issues surveyed in this paper. This work was partially supported by ANR-11-LABX-0040-CIMI (Centre International de Mathématiques et d’Informatique) within the program ANR-11-IDEX-0002-02, project ISIPA.

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    Didier Dubois & Henri Prade

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  3. University of Luxembourg, Esch-Sur-Alzette, Luxembourg

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Dubois, D., Prade, H. (2019). Towards a Reconciliation Between Reasoning and Learning - A Position Paper. In: Ben Amor, N., Quost, B., Theobald, M. (eds) Scalable Uncertainty Management. SUM 2019. Lecture Notes in Computer Science(), vol 11940. Springer, Cham. https://doi.org/10.1007/978-3-030-35514-2_12

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