Analogical Proportions: Why They Are Useful in AI

Prade, Henri; Richard, Gilles

doi:10.24963/ijcai.2021/621

Analogical Proportions: Why They Are Useful in AI

Henri Prade, Gilles Richard

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Survey Track. Pages 4568-4576.

https://doi.org/10.24963/ijcai.2021/621

Keywords

Multidisciplinary topics and applications: General
Knowledge representation and reasoning: General

Abstract

This paper presents a survey of researches in analogical reasoning whose building block are analogical proportions which are statements of the form “a is to b as c is to d”. They have been developed in the last twenty years within an Artificial Intelligence perspective. After discussing their formal modeling with the associated inference mechanism, the paper reports the main results obtained in various AI domains ranging from computational linguistics to classification, including image processing, I.Q. tests, case based reasoning, preference learning, and formal concepts analysis. The last section discusses some new theoretical concerns, and the potential of analogical proportions in other areas such as argumentation, transfer learning, and XAI.

Henri Prade, and Gilles Richard. 2021. Analogical Proportions: Why They Are Useful in AI. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 4568–4576. https://doi.org/10.24963/ijcai.2021/621

@inproceedings{ijcai2021p621,
  title     = {Analogical Proportions: Why They Are Useful in AI},
  author    = {Prade, Henri and Richard, Gilles},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Prof. Zhi-Hua Zhou},
  pages     = {4568--4576},
  year      = {2021},
  month     = {8},
  note      = {Survey Track},
  doi       = {10.24963/ijcai.2021/621},
  url       = {https://doi.org/10.24963/ijcai.2021/621},
}

TY  - CPAPER
TI  - Analogical Proportions: Why They Are Useful in AI
AU  - Prade, Henri
AU  - Richard, Gilles
T2  - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2021
SP  - 4568
EP  - 4576
DO  - 10.24963/ijcai.2021/621
UR  - https://doi.org/10.24963/ijcai.2021/621
ER  -