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The CoRg Project: Cognitive Reasoning

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Abstract

The term cognitive computing refers to new hardware and/or software that mimics the functioning of the human brain. In the context of question answering and commonsense reasoning this means that the reasoning process of humans shall be modeled by adequate technical means. However, since humans do not follow the rules of classical logic, a system designed to model these abilities must be very versatile. The aim of the CoRg project (Cognitive Reasoning) is to successfully complete a reasoning task with commonsense reasoning. We address different benchmarks with focus on the COPA benchmark set (Choice of Plausible Alternatives). Since humans naturally use background knowledge, we have to deal with large background knowledge bases and must be able to reason with multiple input formats and sources in the CoRg system, in order to draw explainable conclusions. For this, we have to find appropriate logics for cognitive reasoning. For a successful reasoning system, nowadays it seems to be important to combine automated reasoning with machine learning technology like recurrent neural networks.

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Notes

  1. https://babelnet.org/, accessed: 11-June-2019

  2. http://conceptnet5.media.mit.edu, accessed: 11-June-2019

  3. https://keras.io/, accessed: 11-June-2019

  4. https://cs-zyluo.github.io/CausalNet/, accessed: 11-June-2019

References

  1. Álvez J, Lucio P, Rigau G (2012) Adimen-SUMO: reengineering an ontology for first-order reasoning. Int J Semant Web Inf Syst 8(4):80–116. https://doi.org/10.4018/jswis.2012100105

    Article  Google Scholar 

  2. Basile V, Cabrio E, Schon C (2016) KNEWS: using logical and lexical semantics to extract knowledge from natural language. In: Proceedings of the European Conference on Artificial Intelligence (ECAI) 2016 conference

  3. Bender M, Pelzer B, Schon C (2013) System description: E-KRHyper 1.4. In: International Conference on automated deduction, pp 126–134. Springer

  4. Byrne RMJ, Johnson-Laird PN (2009) ’if’ and the problems of conditional reasoning. Trends Cogn Sci 13:282–287

    Article  Google Scholar 

  5. Cariani F, Grossi D, Meheus J, Parent X (eds.) (2014) Deontic logic and normative systems—12th International Conference, DEON 2014, Ghent, Belgium, Proceedings, LNAI 8554. Springer. https://doi.org/10.1007/978-3-319-08615-6

  6. d’Avila Garcez AS, Broda K, Gabbay DM (2001) Symbolic knowledge extraction from trained neural networks: a sound approach. Artif Intell 125(1–2):155–207

    Article  MathSciNet  MATH  Google Scholar 

  7. Furbach U, Schon C (2016) Commonsense reasoning meets theorem proving. In: M. Klusch, R. Unland, O. Shehory, A. Pokahr, S. Ahrndt (eds.) Multiagent System Technologies—14th German Conference, MATES 2016, Klagenfurt, Österreich, September 27-30, 2016. Proceedings, Lecture Notes in Computer Science, vol. 9872, pp. 3–17. Springer. https://doi.org/10.1007/978-3-319-45889-2_1

  8. Furbach U, Schon C, Stolzenburg F, Weis KH, Wirth CP (2015) The RatioLog project: rational extensions of logical reasoning. KI 29(3):271–277. https://doi.org/10.1007/s13218-015-0377-9

    Google Scholar 

  9. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  10. Hoder K, Voronkov A (2011) Sine qua non for large theory reasoning. In: Bjørner N, Sofronie-Stokkermans V (eds) Automated deduction - CADE-23, vol 6803. Lecture notes in computer science. Springer, Berlin, pp 299–314. https://doi.org/10.1007/978-3-642-22438-6_23

    Chapter  Google Scholar 

  11. Johnson-Laird PN (1983) Mental models: towards a cognitive science of language, inference, and consciousness. Cambridge University Press, Cambridge

    Google Scholar 

  12. Khemlani SS, Barbey AK, Johnson-Laird PN (2014) Causal reasoning with mental models. Front Hum Neurosci 8:849

    Article  Google Scholar 

  13. Lenat DB (1995) Cyc: a large-scale investment in knowledge infrastructure. Commun ACM 38(11):33–38

    Article  Google Scholar 

  14. Levesque HJ (2011) The winograd schema challenge. In: Logical formalizations of commonsense reasoning, papers from the 2011 AAAI Spring Symposium, Technical Report SS-11-06, Stanford, California, USA, March 21-23, 2011. AAAI. http://www.aaai.org/ocs/index.php/SSS/SSS11/paper/view/2502

  15. Luo Z, Sha Y, Zhu KQ (2016) won Hwang S, Wang Z Commonsense causal reasoning between short texts. In: Proceeding of 15th Int. Conf. on principles of knowledge representation and reasonging (KR’2016). Cape Town, South Africa

  16. Maslan N, Roemmele M, Gordon AS (2015) One hundred challenge problems for logical formalizations of commonsense psychology. In: Twelfth International Symposium on Logical Formalizations of Commonsense Reasoning, Stanford, CA

  17. Miller GA (1995) WordNet: a lexical database for english. Commun ACM 38(11):39–41

    Article  Google Scholar 

  18. Mostafazadeh N, Roth M, Louis A, Chambers N, Allen J (2017) LSDSem 2017 shared task: the story cloze test. In: Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics, pp 46–51

  19. Niles I, Pease A (2001) Towards a standard upper ontology. In: Proceedings of the international conference on formal ontology in information systems-Volume 2001, pp. 2–9. ACM

  20. Nute D (1997) Defeasible deontic logic. In: Synthese library: studies in epistemology, logic, methodology, and philosophy of science, vol. 263. Springer, Berlin. https://doi.org/10.1007/978-94-015-8851-5

  21. Ostermann S, Roth M, Modi A, Thater S, Pinkal M (2018) SemEval-2018 task 11: Machine comprehension using commonsense knowledge. In: Proceedings of the 12th International Workshop on semantic evaluation, pp. 747–757

  22. Roemmele M, Bejan CA, Gordon AS (2011) Choice of plausible alternatives: an evaluation of commonsense causal reasoning. In: AAAI Spring Symposium: logical formalizations of commonsense reasoning

  23. Siebert S, Schon C, Stolzenburg F (2019) Commonsense reasoning using theorem proving and machine learning. In: Holzinger A, Kieseberg P, Weippl E, Tjoa AM (eds) CD-MAKE 2019 – Machine Learning and Knowledge Extraction, LNCS. Springer Nature Switzerland, Canterbury, UK. To appear

  24. Speer R, Chin J, Havasi C (2017) ConceptNet 5.5: an open multilingual graph of general knowledge. In: AAAI Conference on Artificial Intelligence, pp. 4444–4451. http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14972

  25. Spohn W (2012) The laws of belief: ranking theory and its philosophical applications. Oxford University Press, Wiesbaden

    Book  Google Scholar 

  26. Suchanek FM, Kasneci G, Weikum G (2008) YAGO: a large ontology from wikipedia and WordNet. Web Semant 6(3):203–217. https://doi.org/10.1016/j.websem.2008.06.001

    Article  Google Scholar 

  27. Wirth CP, Stolzenburg F (2016) A series of revisions of David Poole’s specificity. Ann Math Artif Intell 78(3):205–258. https://doi.org/10.1007/s10472-015-9471-9 Special issue on Belief Change and Argumentation in Multi-Agent Scenarios. Issue editors: Jürgen Dix, Sven Ove Hansson, Gabriele Kern-Isberner, Guillermo Simari

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Universität Koblenz-Landau, Koblenz, Germany

    Claudia Schon

  2. Harz University of Applied Sciences, Wernigerode, Germany

    Sophie Siebert & Frieder Stolzenburg

Authors
  1. Claudia Schon
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  2. Sophie Siebert
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  3. Frieder Stolzenburg
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Corresponding author

Correspondence to Claudia Schon.

Additional information

The authors gratefully acknowledge the support of the German Research Foundation (DFG) under the grants SCHO 1789/1-1 and STO 421/8-1 CoRg – Cognitive Reasoning.

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Schon, C., Siebert, S. & Stolzenburg, F. The CoRg Project: Cognitive Reasoning. Künstl Intell 33, 293–299 (2019). https://doi.org/10.1007/s13218-019-00601-5

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