Work

On Compatibility between Situation Outcome Cases and Logical Cases

Year: 2025

Type: conference-paper

Abstract: Case-based reasoning (CBR) is central to legal practice, relying on precedents to interpret and apply the law. Various formalisms have been proposed to represent cases, including cases represented as situation-outcome pairs (situation-outcome cases) and cases represented as logical formulas (logical cases). Connections between situation-outcome cases and logical cases have been preliminary explored, but interoperability between CBR models of these representations remains underexamined. To address this gap, this paper introduces four formal tools: (1) compatibility, which concerns interpolating a logical case into multiple situation-outcome cases; (2) enumerators, which generalise compatibility by interpolating logical case models into valid situation-outcome cases; (3) prototypers, which relate logical case models to reflexive and consistent situation-outcome CBR models, as illustrated by AA-CBR and the result model of precedential constraint; and (4) translators, which attempting the reverse, namely connecting such situation-outcome CBR models back to logical case models. Our investigation of these tools reveal how implicit cases can be introduced to simulate or align with another CBR model’s reasoning, which contributes to interoperability between CBR models.

DOI: 10.1145/3769126.3769211

Authors: Wachara Fungwacharakorn, Guilherme Paulino-Passos, Bart Verheij, Ken Satoh

Institutions: Research Organization of Information and Systems, Imperial College London, University of Groningen

Language: English

Topic: AI-based Problem Solving and Planning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Funders: European Research Council, Japan Society for the Promotion of Science

Awards: Development of ODR system by AI, G2431653072

OpenAlex ID: W7123399390

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Cites: 13

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