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.

Ensuring autonomous vehicles (AVs) adhere to traffic rules is crucial for safety. Formalizing these rules into machine-readable formats offers a consistent, unambiguous foundation for automated reasoning and compliance. However, the formalization process is traditionally manual, resource-intensive, and prone to error. This study explores using large language models (LLMs) to automate the translation of traffic rules into PROLOG, a declarative programming language ideal for encoding logical rules and relationships. The proposed methodology consists of three key phases: extracting traffic rules from diverse textual sources, structuring them into Logical English (LE) for clarity and consistency, and translating them into PROLOG representations using advanced natural language processing (NLP) techniques, including in-context learning and fine-tuning. The experimental results demonstrate the effectiveness of LLMs in automating this process, achieving high accuracy in translation. The findings underscore the potential for scaling this methodology to accommodate broader regulatory frameworks, paving the way for safer, more reliable AV operations in complex and dynamic traffic environments.

Precedential constraint is one foundation of case-based reasoning in AI and Law. It generally assumes that the underlying set of precedents must be consistent. To relax this assumption, a generalized notion of the reason model has been introduced. While several argumentative explanation approaches exist for reasoning with precedents based on the traditional consistent reason model, there has been no corresponding argumentative explanation method developed for this generalized reasoning framework accommodating inconsistent precedents. To address this question, this paper examines an extension of the derivation state argumentation framework (DSA-framework) to explain the reasoning according to the generalized notion of the reason model.

To exhaustively understand the impact of rule amendments and unforeseen cases on existing norms, it requires connecting their rule-based and case-based representations. However, those connections have not been explored in depth, especially for norms that are represented as soft constraints. This paper aims to explore the connection between constraint hierarchies and case models as representative formalisms of rule-based and case-based representations of soft-constraint norms respectively. To explore the connection, we express norm scopes and preferences in both formalisms as diagrams. Based on tightening and arranging diagrams, we found the translation of constraint hierarchies with one constraint per level into case models. This provides new insights into understanding prototypical cases made by rule-based soft-constraint norms.

Large Language Models (LLMs) face limitations in logical reasoning, which restrict their applicability in critical domains such as law. Current evaluation methods often lead to inaccurate assessments of LLMs’ capabilities due to their simplicity. This paper presents a refined evaluation method for assessing LLMs’ capability to answer legal questions by eliminating the possibility of obtaining correct responses by chance. Furthermore, we introduce the LogiLaw dataset, which aims to enhance the models’ logical reasoning capacities in general and legal reasoning specifically. By leveraging the refined evaluation technique, the LogiLaw dataset, and the proposed Reinforcement Learning from Logical Feedback (RLLF) approach, our work aims to open new avenues for research to bolster LLMs’ performance in law and other logic-intensive disciplines while addressing the shortcomings of conventional evaluation approaches.

This paper studies constraint hierarchies for ethical norms, which are unwritten and may be relaxed if they conflict with stronger norms. Since such ethical norms are unwritten, initial representations of ethical norms may contain errors. For correcting those errors, this paper examines fundamental revisions on constraint hierarchies for ethical norms. Although some revisions on representations for ethical norms have been suggested, revisions on constraint hierarchies for ethical norms have not been completely investigated. In this paper, we categorize two fundamental types of revisions on such constraint hierarchies, namely preference revision and content revision. We also compare effects of those revisions in the criteria of syntactic and semantic changes, which are common criteria of revisions on legal theories. From the comparison, we found that preference revision tentatively makes lower syntactic changes. However, its computation is intractable, incomplete, and potentially makes a large number of semantic changes. On the other hand, we show that content revision on constraint hierarchies can make a small number of semantic changes. However, the content revision tentatively produce a large number of syntactic changes. This comparison leads to the possibility of optimization between preference revision and content revision, which we think is an interesting future work.

Translating often has the meaning of converting from one human language to another. However, in a broader sense, it means transforming a message from one form of communication to another form. Logic is an important form of communication and the ability to translate natural language into logic is important in many different fields, in which logical reasoning and logical arguments are used. In the legal field, for example, judges must often reason from facts and arguments presented in natural language to logical conclusions. In this paper, toward the goal of support for this kind of reasoning with machines, we propose a method for translating natural language into logical representations using a combination of deep learning methods. Our approach contributes methodologies and insights to the development of computational methods for converting natural language into logical representations.

When literal interpretation of statutes leads to counterintuitive consequences, judges, especially in high courts, may identify counterintuitive consequences and revise interpretation of statutes. Researchers have studied revisions for computational legal representation. Generally, studies on revision usually consider minimal revision to reflect limitation of judges' legislative power. However, those studies tend to minimize the number of operations used for changing rules rather than minimize the changes of semantics (the set of conclusions obtained from the program), which vary among cases. In this paper, we consider minimizing the changes of semantics of a rule-base written in a normal logic program. We consider that each possible fact-base (the representation of a case) has its corresponding semantics and corresponding dominant rule-base, which is a set of Horn clauses obtained from the subset of rule-base that is specific to the considered fact-base. Hence, we present a new sub type of semantics-based minimal revision called a dominant-based minimal revision. Furthermore, we present one guidance to obtain one dominant-based minimal revision by using legal debugging and Closed World Specification. We also compare the dominant-based minimal revision with the syntax-based minimal revision in Theory Distance Metric. As the syntax-based minimal revision minimizes the number of operations used for changing rules, the comparison shows that the syntax-based minimal revision may cause extra semantics changes compared to the dominant-based minimal revision, especially when the rule-base contains multiple rules for the same consequence. We discuss that such extra semantics changes can be considered as unintentional changes caused by the syntax-based minimal revision. Hence, legal reasoning systems can check with the user such extra semantics changes to confirm the user intention of changes.

Since the legal rules cannot be perfect, we have proposed a work called Legal Debugging for handling counterintuitive consequences caused by imperfection of the law. Legal debugging consists of two steps. Firstly, legal debugging interacts with a judge as an oracle that gives the intended interpretation of the law and collaboratively figures out a legal rule called a culprit, which determines as a root cause of counterintuitive consequences. Secondly, the legal debugging determines possible resolutions for a culprit . The way we have proposed to resolve a culprit is to use extra facts that have not been considered in the legal rules to describe the exceptional situation of the case. Nevertheless, the result of the resolution is usually considered as too specific and no generalizations of the resolution are provided. Therefore, in this paper, we introduce a rule generalization step into Legal Debugging. Specifically, we have reorganized Legal Debugging into four steps, namely a culprit detection, an exception invention, a fact-based induction, and a rule-based induction. During these four steps, a new introduced rule is specific at first then becomes more generalized. This new step allows a user to use existing legal concepts from the background knowledge for revising and generalizing legal rules.