Deontic Meta-Rules

• URL: http://arxiv.org/abs/2209.12655v1
• Date: Fri, 23 Sep 2022 07:48:29 GMT
• Title: Deontic Meta-Rules
• Authors: Francesco Olivieri, Guido Governatori, Matteo Cristani, Antonino Rotolo and Abdul Sattar
• Abstract summary: This work extends such a logical framework by considering the deontic aspect. The resulting logic will not just be able to model policies but also tackle well-known aspects that occur in numerous legal systems.
• Score: 2.241042010144441
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The use of meta-rules in logic, i.e., rules whose content includes other rules, has recently gained attention in the setting of non-monotonic reasoning: a first logical formalisation and efficient algorithms to compute the (meta)-extensions of such theories were proposed in Olivieri et al (2021) This work extends such a logical framework by considering the deontic aspect. The resulting logic will not just be able to model policies but also tackle well-known aspects that occur in numerous legal systems. The use of Defeasible Logic (DL) to model meta-rules in the application area we just alluded to has been investigated. Within this line of research, the study mentioned above was not focusing on the general computational properties of meta-rules. This study fills this gap with two major contributions. First, we introduce and formalise two variants of Defeasible Deontic Logic with Meta-Rules to represent (1) defeasible meta-theories with deontic modalities, and (2) two different types of conflicts among rules: Simple Conflict Defeasible Deontic Logic, and Cautious Conflict Defeasible Deontic Logic. Second, we advance efficient algorithms to compute the extensions for both variants.

Related papers

• LogicGame: Benchmarking Rule-Based Reasoning Abilities of Large Language Models [87.49676980090555]
  Large Language Models (LLMs) have demonstrated notable capabilities across various tasks, showcasing complex problem-solving abilities. We introduce LogicGame, a novel benchmark designed to evaluate the comprehensive rule understanding, execution, and planning capabilities of LLMs.
  arXiv  Detail & Related papers  (2024-08-28T13:16:41Z)
• Can LLMs Reason with Rules? Logic Scaffolding for Stress-Testing and Improving LLMs [87.34281749422756]
  Large language models (LLMs) have achieved impressive human-like performance across various reasoning tasks. However, their mastery of underlying inferential rules still falls short of human capabilities. We propose a logic scaffolding inferential rule generation framework, to construct an inferential rule base, ULogic.
  arXiv  Detail & Related papers  (2024-02-18T03:38:51Z)
• Chain of Logic: Rule-Based Reasoning with Large Language Models [10.017812995997753]
  Rule-based reasoning enables us to draw conclusions by accurately applying a rule to a set of facts. We introduce a new prompting method, Chain of Logic, which elicits rule-based reasoning through decomposition and recomposition. We evaluate chain of logic across eight rule-based reasoning tasks involving three distinct compositional rules from the LegalBench benchmark.
  arXiv  Detail & Related papers  (2024-02-16T01:54:43Z)
• ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoning [107.61997887260056]
  We propose a novel framework, ChatRule, unleashing the power of large language models for mining logical rules over knowledge graphs. Specifically, the framework is initiated with an LLM-based rule generator, leveraging both the semantic and structural information of KGs. To refine the generated rules, a rule ranking module estimates the rule quality by incorporating facts from existing KGs.
  arXiv  Detail & Related papers  (2023-09-04T11:38:02Z)
• A Semantic Approach to Decidability in Epistemic Planning (Extended Version) [72.77805489645604]
  We use a novel semantic approach to achieve decidability. Specifically, we augment the logic of knowledge S5$_n$ and with an interaction axiom called (knowledge) commutativity. We prove that our framework admits a finitary non-fixpoint characterization of common knowledge, which is of independent interest.
  arXiv  Detail & Related papers  (2023-07-28T11:26:26Z)
• Avoiding Pragmatic Oddity: A Bottom-up Defeasible Deontic Logic [1.160208922584163]
  This paper presents an extension of Defeasible Deontic Logic to deal with the Pragmatic Oddity problem. The logic applies three general principles: (1) the Pragmatic Oddity problem must be solved within a general logical treatment of CTD reasoning; (2) non-monotonic methods must be adopted to handle CTD reasoning; and (3) logical models of CTD reasoning must be computationally feasible and, if possible, efficient.
  arXiv  Detail & Related papers  (2022-09-09T23:14:09Z)
• Automating Defeasible Reasoning in Law [0.0]
  We study defeasible reasoning in rule-based systems, in particular about legal norms and contracts. We identify rule modifier that specify how rules interact and how they can be overridden. We then define rule transformations that eliminate these modifier, leading to a translation of rules to formulas.
  arXiv  Detail & Related papers  (2022-05-15T17:14:15Z)
• Neuro-Symbolic Hierarchical Rule Induction [12.610497441047395]
  We propose an efficient interpretable neuro-symbolic model to solve Inductive Logic Programming (ILP) problems. In this model, which is built from a set of meta-rules organised in a hierarchical structure, first-order rules are invented by learning embeddings to match facts and body predicates of a meta-rule. We empirically validate our model on various tasks (ILP, visual genome, reinforcement learning) against several state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-12-26T17:02:14Z)
• Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks [65.23508422635862]
  We propose learning rules with the recently proposed logical neural networks (LNN) Compared to others, LNNs offer strong connection to classical Boolean logic. Our experiments on standard benchmarking tasks confirm that LNN rules are highly interpretable.
  arXiv  Detail & Related papers  (2021-12-06T19:38:30Z)
• RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs [91.71504177786792]
  This paper studies learning logic rules for reasoning on knowledge graphs. Logic rules provide interpretable explanations when used for prediction as well as being able to generalize to other tasks. Existing methods either suffer from the problem of searching in a large search space or ineffective optimization due to sparse rewards.
  arXiv  Detail & Related papers  (2020-10-08T14:47:02Z)
• Public Announcement Logic in HOL [0.0]
  shallow semantical embedding for public announcement logic with relativized common knowledge is presented. This embedding enables the first-time automation of this logic with off-the-shelf theorem provers for classical higher-order logic.
  arXiv  Detail & Related papers  (2020-10-02T06:46:02Z)

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.