Implicitly Learning to Reason in First-Order Logic (Extended Abstract)

Vaishak Belle, Brendan Juba

Research output: Contribution to conferenceAbstractpeer-review

Abstract

The tension between deduction and induction is perhaps the most fundamental issue in areas such as philosophy, cognition and artificial intelligence. 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 an influential paper, Valiant (2000) recognized that the challenge of learning should be integrated with deduction. In particular, he proposed a semantics to capture the quality possessed by the output of (probably approximately correct) PAC-learning algorithms when formulated in a logic. Although weaker than classical entailment, it allows for a powerful model theoretic framework for answering queries.
Original languageEnglish
Number of pages2
Publication statusPublished - 12 Sep 2020
Event17th International Conference on Principles of Knowledge Representation and Reasoning - Rhodes, Greece
Duration: 12 Sep 202018 Sep 2020
https://kr2020.inf.unibz.it/

Conference

Conference17th International Conference on Principles of Knowledge Representation and Reasoning
Abbreviated titleKR 2020
Country/TerritoryGreece
CityRhodes
Period12/09/2018/09/20
Internet address

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  • Implicitly Learning to Reason in First-Order Logic

    Belle, V. & Juba, B., 14 Dec 2019, Advances in Neural Information Processing Systems 32 (NeurIPS 2019). Neural Information Processing Systems, Vol. 32. p. 3381-3391 11 p. (Advances in Neural Information Processing Systems; vol. 32).

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