Analogical Reasoning, Generalization, and Rule Learning for Common Law Reasoning

Joseph Blass; Kenneth D. Forbus

doi:10.1145/3594536.3595121

Analogical Reasoning, Generalization, and Rule Learning for Common Law Reasoning

Joseph Blass, Kenneth D. Forbus

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

Research in AI & Law has sought to model common-law case-based reasoning by creating analogies from cases, extracting and applying rules from cases, or both. This paper presents a new approach to extracting legal information from cases and several methods to apply it to new cases, including by analogy and by conversion to logical rules. It evaluates the approaches on a dataset of real-world cases and compares the results to off-the-shelf machine-learning techniques. We conclude that abstract legal information can be extracted from similar cases through analogical generalization, and that the extracted legal schemas can be used to reason about and solve other cases both by analogy and by rules.

Original language	English (US)
Title of host publication	19th International Conference on Artificial Intelligence and Law, ICAIL 2023 - Proceedings of the Conference
Publisher	Association for Computing Machinery, Inc
Pages	32-41
Number of pages	10
ISBN (Electronic)	9798400701979
DOIs	https://doi.org/10.1145/3594536.3595121
State	Published - Jun 19 2023
Event	19th International Conference on Artificial Intelligence and Law, ICAIL 2023 - Braga, Portugal Duration: Jun 19 2023 → Jun 23 2023

Publication series

Name	19th International Conference on Artificial Intelligence and Law, ICAIL 2023 - Proceedings of the Conference

Conference

Conference	19th International Conference on Artificial Intelligence and Law, ICAIL 2023
Country/Territory	Portugal
City	Braga
Period	6/19/23 → 6/23/23

Funding

This research was supported by the Computational Cognition and Machine Intelligence Program of the Air Force Office of Scientific Research under award #FA9550-20-1-0091.

Keywords

Analogy
Legal Schemas
Precedential Reasoning
Rule Learning

ASJC Scopus subject areas

Artificial Intelligence
Software
Law

Access to Document

10.1145/3594536.3595121

Cite this

Blass, J., & Forbus, K. D. (2023). Analogical Reasoning, Generalization, and Rule Learning for Common Law Reasoning. In 19th International Conference on Artificial Intelligence and Law, ICAIL 2023 - Proceedings of the Conference (pp. 32-41). (19th International Conference on Artificial Intelligence and Law, ICAIL 2023 - Proceedings of the Conference). Association for Computing Machinery, Inc. https://doi.org/10.1145/3594536.3595121

Blass, Joseph ; Forbus, Kenneth D. / Analogical Reasoning, Generalization, and Rule Learning for Common Law Reasoning. 19th International Conference on Artificial Intelligence and Law, ICAIL 2023 - Proceedings of the Conference. Association for Computing Machinery, Inc, 2023. pp. 32-41 (19th International Conference on Artificial Intelligence and Law, ICAIL 2023 - Proceedings of the Conference).

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abstract = "Research in AI & Law has sought to model common-law case-based reasoning by creating analogies from cases, extracting and applying rules from cases, or both. This paper presents a new approach to extracting legal information from cases and several methods to apply it to new cases, including by analogy and by conversion to logical rules. It evaluates the approaches on a dataset of real-world cases and compares the results to off-the-shelf machine-learning techniques. We conclude that abstract legal information can be extracted from similar cases through analogical generalization, and that the extracted legal schemas can be used to reason about and solve other cases both by analogy and by rules.",

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Blass, J & Forbus, KD 2023, Analogical Reasoning, Generalization, and Rule Learning for Common Law Reasoning. in 19th International Conference on Artificial Intelligence and Law, ICAIL 2023 - Proceedings of the Conference. 19th International Conference on Artificial Intelligence and Law, ICAIL 2023 - Proceedings of the Conference, Association for Computing Machinery, Inc, pp. 32-41, 19th International Conference on Artificial Intelligence and Law, ICAIL 2023, Braga, Portugal, 6/19/23. https://doi.org/10.1145/3594536.3595121

Analogical Reasoning, Generalization, and Rule Learning for Common Law Reasoning. / Blass, Joseph; Forbus, Kenneth D.
19th International Conference on Artificial Intelligence and Law, ICAIL 2023 - Proceedings of the Conference. Association for Computing Machinery, Inc, 2023. p. 32-41 (19th International Conference on Artificial Intelligence and Law, ICAIL 2023 - Proceedings of the Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Forbus, Kenneth D.

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AB - Research in AI & Law has sought to model common-law case-based reasoning by creating analogies from cases, extracting and applying rules from cases, or both. This paper presents a new approach to extracting legal information from cases and several methods to apply it to new cases, including by analogy and by conversion to logical rules. It evaluates the approaches on a dataset of real-world cases and compares the results to off-the-shelf machine-learning techniques. We conclude that abstract legal information can be extracted from similar cases through analogical generalization, and that the extracted legal schemas can be used to reason about and solve other cases both by analogy and by rules.

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Blass J, Forbus KD. Analogical Reasoning, Generalization, and Rule Learning for Common Law Reasoning. In 19th International Conference on Artificial Intelligence and Law, ICAIL 2023 - Proceedings of the Conference. Association for Computing Machinery, Inc. 2023. p. 32-41. (19th International Conference on Artificial Intelligence and Law, ICAIL 2023 - Proceedings of the Conference). doi: 10.1145/3594536.3595121

Analogical Reasoning, Generalization, and Rule Learning for Common Law Reasoning

Abstract

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Keywords

ASJC Scopus subject areas

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