We extend the formal framework of classifier models used in the legal domain. While the existing classifier framework characterises cases solely through the facts involved, legal reasoning fundamentally relies on both facts and rules, particularly the ratio decidendi. This paper presents an initial approach to incorporating sets of rules within a classifier. Our work is built on the work of Canavotto et al. (2023), which has developed the rule-based reason model of precedential constraint within a hierarchy of factors. We demonstrate how decisions for new cases can be inferred using this enriched rule-based classifier framework. Additionally, we provide an example of how the time element and the hierarchy of courts can be used in the new classifier framework

We present a rights-first model of contrary-to-duty (CTD) reasoning with two remedial regimes and a revision track. In the CTD-Claim regime, when a primary duty is not fulfilled and no exception applies, a remedial claim detaches automatically, without any recognition act. In the CTD-Power regime, a remedial claim arises only if the rights-holder exercises a recognition power; until then there is no recognised violation and no remedial duty. Revision-of-Duty (RoD) is an alternative discretionary power that adapts the primary duty without recognising a violation, keeping the purpose aligned and avoiding sanctions. Under CTD-Power, revision competes directly with recognition on the same case. We develop a model for agreements, give concise dynamic-logic-style specifications of guards and acts, implement an institutional rights system that executes these specifications over live Hohfeldian bundles (with per-case exclusivity, exception handling and provenance), and show how agentic AI can use reasons to choose among the admissible acts within the rights-first framework.

Consistency of case bases is a way to avoid the problem of retrieving conflicting constraining precedents for new cases to be decided. However, in legal practice the consistency requirements for cases bases may not be satisfied. As pointedout in [6], a model of precedential constraint should take into account the hierarchical structure of the specific legal system under consideration and the temporaldimension of cases. This article continues the research initiated in [18,9], whichestablished a connection between Boolean classifiers and legal case-based reasoning. On this basis, we enrich the classifier models with an organisational structurethat takes into account both the hierarchy of courts and which courts issue decisions that are binding/constraining on subsequent cases. We focus on common lawsystems. We also introduce a temporal relation between cases. Within this enrichedframework, we can formalise the notions of overruled cases and cases decided perincuriam: such cases are not to be considered binding on later cases. Finally, weshow under which condition principles based on the hierarchical structure and onthe temporal dimension can provide an unambiguous decision-making process fornew cases in the presence of conflicting binding precedents.

Abstract This paper develops a deontic logic for defeasible permission and studies norm change in various updated semantics. When one grants that $\varphi $ or $\psi $ is permitted, normally, it goes together with the conjunction of a permission of $\varphi $ and that of $\psi $. In the monotonic reasoning on this permission, a permission of $\varphi $ leads to a permission of $\varphi $ and $\psi $; however, if a prohibition of $\psi $ is introduced, we get into trouble. We face a paradox of free choice permission. Many solutions have been proposed, but a systematic account of handling norm change is still needed. This paper first introduces the notion of normality to develop a sound and complete deontic logic for defeasible permission, which can be used to analyse several notions in natural language and in game theory. Further, following Lewis’ idea of norm change, a systematic way to capture various dynamics for updating permission and obligation is proposed.

With the development of next generation artificial intelligence, the interplay between knowledge-based approaches and machine learning approaches is gaining momentum. This trend drives the researchers to pay attention to possible and novel developments in relevant areas, either based on their theoretical foundations or based on their efficient applications. This special issue collects the newly developed works from logic, argumentation and artificial intelligence, to stimulate possible outcomes from their interactions. This volume includes 14 papers selected from 25 submissions accepted to the 2020 International Conferences on Logic and Artificial Intelligence at Zhejiang University (ZJULogAI). ZJULogAI was jointly organized by the University of Luxembourg and Zhejiang University as a three-day digital online event. It consists of three sub-events: the 5th Asian Workshop on Philosophical Logic (AWPL 2020), the 3rd International Conference on Logic and Argumentation (CLAR 2020) and the 6th Global Conference on Artificial Intelligence (GCAI 2020). Five experts (Christoph Benzmüller, Claude Sammut, Dag Westerståhl, Fei Wu and Marc van Zee) in logic and AI were invited to present their current work, covering the topics of ethical-legal AI systems, logic-based robots, big data intelligence and natural language and AI.