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Unified Decomposition-Aggregation (UDA) Rules: Dynamic, Schematic, Novel Axioms

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13467))

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Abstract

We introduce Unified Decomposition-Aggregation (UDA) Rules. They are a family of axiom schemata that are instantiated at run-time to add new axioms to a logical theory. These new axioms are implications, whose preconditions will be constructed from an analysis of the goal to be proved and the theory in which it is to be proved. We illustrate their application to query answering using the FRANK system.

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Notes

  1. 1.

    Previously called decomposition rules.

  2. 2.

    See https://en.wikipedia.org/wiki/Association_list (last accessed: 02-02-2022). Alists are not lists but sets, but the ‘alist’ terminology has, unfortunately, become standard.

  3. 3.

    https://en.wikipedia.org/wiki/Epsilon_calculus accessed on 02.02.2022.

  4. 4.

    Although not for any of the examples in this paper.

  5. 5.

    Or similar, depending on the statistical methods used.

  6. 6.

    Note that the temporal rule can also use non-statistical aggregation functions, e.g., temp(max) could be used to find the maximum value of a property among a set of times.

  7. 7.

    https://en.wikipedia.org/wiki/68-95-99.7_rule accessed on 12.5.22.

  8. 8.

    We are grateful to an anonymous reviewer for pointing out this analogy and suggesting that we discuss it here.

References

  1. Bachmair, L., Ganzinger, H.: Resolution theorem proving. In: Robinson, J.A., Voronkov, A. (eds.) Handbook of Automated Reasoning, vol. 1, vol. I, chap. 2, pp. 19–199. Elsevier (2001)

    Google Scholar 

  2. Bundy, A.: A Science of Reasoning, pp. 178–198. MIT Press, Cambridge (1991)

    Google Scholar 

  3. Bundy, A., Byrd, L., Luger, G., Mellish, C., Milne, R., Palmer, M.: Solving mechanics problems using meta-level inference. In: Buchanan, B.G. (ed.) Proceedings of IJCAI-79, pp. 1017–1027. International Joint Conference on Artificial Intelligence (1979)

    Google Scholar 

  4. Bundy, A., Nuamah, K.: Combining deductive and statistical explanations in the FRANK query answering system. In: Gong, Z., Li, X., Oguducu, S.G. (eds.) Proceedings of the 12th IEEE International Conference on Big Knowledge (ICBK), IEEE, Auckland, New Zealand, December 2021

    Google Scholar 

  5. 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

    Chapter  Google Scholar 

  6. Das, R., et al.: Multi-step entity-centric information retrieval for multi-hop question answering. In: Proceedings of the 2nd Workshop on Machine Reading for Question Answering, pp. 113–118 (2019)

    Google Scholar 

  7. Fierens, D., et al.: Inference and learning in probabilistic logic programs using weighted boolean formulas. Theor. Pract. Logic Program. 15(3), 358–401 (2015)

    Article  MathSciNet  Google Scholar 

  8. Fletcher, T., Bundy, A., Nuamah, K.: Statistics automation in a query-answering system. Technical report, The University of Edinburgh (2022)

    Google Scholar 

  9. Nilsson, N.J.: Probabilistic logic. Artif. Intell. 28(1), 71–87 (1986)

    Article  MathSciNet  Google Scholar 

  10. Nuamah, K.: Functional inferences over heterogeneous data, unpublished Ph.D. Dissertation, University of Edinburgh (2018)

    Google Scholar 

  11. Nuamah, K.: Deep algorithmic question answering: towards a compositionally hybrid AI for algorithmic reasoning. In: Workshop on Knowledge Representation for Hybrid and Compositional AI (2021)

    Google Scholar 

  12. Nuamah, K., Bundy, A.: Calculating error bars on inferences from web data. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) IntelliSys 2018. AISC, vol. 869, pp. 618–640. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-01057-7_48

    Chapter  Google Scholar 

  13. Nuamah, K., Bundy, A., Jia, Y.: A context mechanism for an inference-based question answering system. In: AAAI2021 Workshop on CSKGs, vol. 8 (2021)

    Google Scholar 

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Acknowledgements

Thanks to Nicholas Ferguson, Thomas Fletcher, Xue Li, Ruqui Zhu and five anonymous reviewers for feedback on an earlier draft. This research has been supported by Huawei grants CIENG4721/LSC and HO2017050001B8s. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.

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Bundy, A., Nuamah, K. (2022). Unified Decomposition-Aggregation (UDA) Rules: Dynamic, Schematic, Novel Axioms. In: Buzzard, K., Kutsia, T. (eds) Intelligent Computer Mathematics. CICM 2022. Lecture Notes in Computer Science(), vol 13467. Springer, Cham. https://doi.org/10.1007/978-3-031-16681-5_15

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  • DOI: https://doi.org/10.1007/978-3-031-16681-5_15

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