Formalizing Arguments, Rules and Cases

Verheij, B., 2017, ICAIL '17: Proceedings of the 16th international conference on Artificial int. London: Association for Computing Machinery, p. 199-208 10 p. (Proceedings of the 16th international conference on Artificial intelligence and law; vol. 10).

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

Legal argument is typically backed by two kinds of sources: cases and rules. In much AI & Law research, the formalization of argu-ments, rules and cases has been investigated. In this paper, the tight formal connections between the three are developed further, in an attempt to show that cases can provide the logical basis for estab-lishing which rules and arguments hold in a domain. We use the recently proposed formalism of case models, that has been applied previously to evidential reasoning and ethical systems design. In the present paper, we discuss with respect to case-based modeling how the analogy and distinction between cases can be modeled, and how arguments can be grounded in cases. With respect to rule-based modeling, we discuss conditionality, generality and chaining. With respect to argument-based modeling, we discuss rebutting, undercutting and undermining attack. We evaluate the approach by developing a case model of the rule-based arguments and attacks in Dutch tort law. In this way, we illustrate how statutory, rule-based law from the civil law tradition can be formalized in terms of cases.
Original languageEnglish
Title of host publicationICAIL '17
Subtitle of host publicationProceedings of the 16th international conference on Artificial int
Place of PublicationLondon
PublisherAssociation for Computing Machinery
Number of pages10
ISBN (Print)9781450348911
Publication statusPublished - 2017

Publication series

NameProceedings of the 16th international conference on Artificial intelligence and law


  • @BULLET Applied computing → Law, @BULLET The-ory of computation → Logic, Case-based reasoning, KEYWORDS Argumentation, Rule-based reasoning

ID: 66171507