A Modal Logic for Temporal and Jurisdictional Classifier Models

• URL: http://arxiv.org/abs/2510.13691v1
• Date: Wed, 15 Oct 2025 15:50:04 GMT
• Title: A Modal Logic for Temporal and Jurisdictional Classifier Models
• Authors: Cecilia Di Florio, Huimin Dong, Antonino Rotolo,
• Abstract summary: We introduce a modal logic of classifiers designed to formally capture legal case-based reasoning (CBR)<n>We incorporate principles for resolving conflicts between precedents, by introducing into the logic the temporal dimension of cases and the hierarchy of courts within the legal system.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Logic-based models can be used to build verification tools for machine learning classifiers employed in the legal field. ML classifiers predict the outcomes of new cases based on previous ones, thereby performing a form of case-based reasoning (CBR). In this paper, we introduce a modal logic of classifiers designed to formally capture legal CBR. We incorporate principles for resolving conflicts between precedents, by introducing into the logic the temporal dimension of cases and the hierarchy of courts within the legal system.

Related papers

• XAI-LAW: A Logic Programming Tool for Modeling, Explaining, and Learning Legal Decisions [0.5672012177310438]
  We propose an approach to model articles of the Italian Criminal Code (ICC) using Answer Set Programming (ASP)<n>The developed tool is intended to support legal experts during the criminal trial phase by providing reasoning and possible legal outcomes.
  arXiv  Detail & Related papers  (2026-01-07T12:07:30Z)
• DivLogicEval: A Framework for Benchmarking Logical Reasoning Evaluation in Large Language Models [58.439517684779936]
  This paper proposes a new classical logic benchmark DivLogicEval, consisting of natural sentences composed of diverse statements in a counterintuitive way.<n>To ensure a more reliable evaluation, we also introduce a new evaluation metric that mitigates the influence of bias and randomness inherent in Large Language Models.
  arXiv  Detail & Related papers  (2025-09-19T04:40:46Z)
• A Reduction of Input/Output Logics to SAT [51.82266520875928]
  Deontic logics are formalisms for reasoning over norms, obligations, permissions and prohibitions.<n>In this paper, an automation approach for I/O logics is presented that makes use of suitable reductions to propositional satisfiability problems.
  arXiv  Detail & Related papers  (2025-08-22T09:22:26Z)
• RLJP: Legal Judgment Prediction via First-Order Logic Rule-enhanced with Large Language Models [58.69183479148083]
  Legal Judgment Prediction (LJP) is a pivotal task in legal AI.<n>Existing LJP models integrate judicial precedents and legal knowledge for high performance.<n>But they neglect legal reasoning logic, a critical component of legal judgments requiring rigorous logical analysis.<n>This paper proposes a rule-enhanced legal judgment prediction framework based on first-order logic (FOL) formalism and comparative learning (CL)
  arXiv  Detail & Related papers  (2025-05-27T14:50:21Z)
• Rule-based Classifier Models [0.4915744683251149]
  This paper presents an initial approach to incorporating sets of rules within a classifier.<n>We demonstrate how decisions for new cases can be inferred using this enriched rule-based framework.
  arXiv  Detail & Related papers  (2025-05-01T11:59:16Z)
• Logic Rules as Explanations for Legal Case Retrieval [9.240902132139187]
  We propose a framework that conducts reasoning on the matching of legal cases through learning case-level and law-level logic rules. Benefiting from the logic and interpretable nature of the logic rules, NS-LCR is equipped with built-in faithful explainability.
  arXiv  Detail & Related papers  (2024-03-03T09:22:21Z)
• Automated legal reasoning with discretion to act using s(LAW) [0.294944680995069]
  ethical and legal concerns make it necessary for automated reasoners to justify in human-understandable terms. We propose to use s(CASP), a top-down execution model for predicate ASP, to model vague concepts following a set of patterns. We have implemented a framework, called s(LAW), to model, reason, and justify the applicable legislation and validate it by translating (and benchmarking) a representative use case.
  arXiv  Detail & Related papers  (2024-01-25T21:11:08Z)
• An Encoding of Abstract Dialectical Frameworks into Higher-Order Logic [57.24311218570012]
  This approach allows for the computer-assisted analysis of abstract dialectical frameworks. Exemplary applications include the formal analysis and verification of meta-theoretical properties.
  arXiv  Detail & Related papers  (2023-12-08T09:32:26Z)
• Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration [52.57055162778548]
  Legal Judgment Prediction (LJP) has become an increasingly crucial task in Legal AI. Precedents are the previous legal cases with similar facts, which are the basis for the judgment of the subsequent case in national legal systems. Recent advances in deep learning have enabled a variety of techniques to be used to solve the LJP task.
  arXiv  Detail & Related papers  (2023-10-13T16:47:20Z)
• Modelling and Explaining Legal Case-based Reasoners through Classifiers [9.052343834206992]
  This paper brings together two lines of research: factor-based models of case-based reasoning (CBR) and the logical specification of classifiers.
  arXiv  Detail & Related papers  (2022-10-20T12:51:12Z)
• A Formalisation of Abstract Argumentation in Higher-Order Logic [77.34726150561087]
  We present an approach for representing abstract argumentation frameworks based on an encoding into classical higher-order logic. This provides a uniform framework for computer-assisted assessment of abstract argumentation frameworks using interactive and automated reasoning tools.
  arXiv  Detail & Related papers  (2021-10-18T10:45:59Z)
• Modelling Value-oriented Legal Reasoning in LogiKEy [0.0]
  We show how LogiKEy can harness interactive and automated theorem proving technology to provide a testbed for the development and formal verification of legal domain-specific languages and theories. We establish novel bridges between latest research in knowledge representation and reasoning in non-classical logics, automated theorem proving, and applications in legal reasoning.
  arXiv  Detail & Related papers  (2020-06-23T06:57:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.