Arg-tuProlog: A Modular Logic Argumentation Tool for PIL

open5noPrivate international law (PIL) addresses overlaps and conflicts between legal systems by distributing cases between the authorities of such systems (jurisdiction) and establishing what rules these authorities have to apply to each case(choice of law). A modular argumentation tool, Arg-tuProlog, is here presented that enables reasoning with rules and interpretations of multiple legal systems.Roberta Calegari and Giovanni Sartor have been supported by the H2020 ERC Project “CompuLaw” (G.A. 833647).Giuseppe Contissa, Giuseppe Pisano and Galileo Sartor have been supported by the European Union’s Justice programme under Grant Agreement No. 800839 for the project “InterLex: Advisory and Training System for Internet-related private International Law”.openCalegari, Roberta; Contissa, Giuseppe; Pisano, Giuseppe; Sartor, Galileo; Sartor, GiovanniCalegari, Roberta; Contissa, Giuseppe; Pisano, Giuseppe; Sartor, Galileo; Sartor, Giovann

AI simbolica e sub-simbolica per XAI: stato dell'arte ed esperimenti con reti neurali e vincoli logici

L'intelligenza artificiale ha visto nel tempo il delinearsi di due paradigmi distinti: quello simbolico, basato sulla manipolazione di simboli come metodo di approssimazione dell’intelligenza umana, e quello sub-simbolico, basato invece sull’applicazione di procedure statistiche o numeriche. Sebbene il secondo goda oggi di un rinnovato successo -- anche grazie ai vantaggi dal punto di vista di scalabilità e capacità di gestione della conoscenza contestuale --, esso manca di uno dei principali pregi delle tecniche simboliche: la comprensibilità. Le tecniche sub-simboliche infatti producono spesso predittori difficilmente comprensibili ad un osservatore umano, rendendo le decisioni basate su essi difficilmente interpretabili. D'altra parte, i modelli simbolici -- che viceversa sono facilmente comprensibili -- non sono arrivati finora ad un'ampia diffusione, presentando limiti sia in termini di performance che in termini di capacità di apprendere. Muovendo da queste considerazioni, è nato un nuovo campo di ricerca, che mira ad unificare e sfruttare in maniera sinergica il paradigma simbolico e sub-simbolico. Le tecniche ibride -- che combinano cioè i due approcci a livello di modello -- potrebbero fornire la chiave per il superamento dei limiti di entrambi, sfruttandone al contempo i pregi, a particolare beneficio della spiegabilità dei sistemi intelligenti. Tra gli ambiti potenzialmente intersecati con questo nuovo campo di ricerca c'è il ramo della Computazione Neuro Simbolica (NSC), che corrisponde all'oggetto di analisi ed esplorazione di questa tesi. Il primo obiettivo della tesi è fornire una rassegna della letteratura in ambito NSC, finalizzata a valutarne l'impiego nella creazione di sistemi intelligenti spiegabili. Come secondo obiettivo, la tesi si occupa di discutere un possibile modello di integrazione, e della sua prototipazione e validazione, selezionando le tecnologie più adatte alla sua realizzazione nel campo della Computazione Neuro Simbolica

Argumentation for Legal Reasoning: Meta-models, Technology and Beyond

This thesis presents a comprehensive exploration of argumentation in the context of legal reasoning, bridging the gap between formal argumentation theory and its technological applications. Central to this work is the enhancement of the ASPIC+ framework, integrating structured meta-argumentation to address limitations in reasoning about rules, conflicts, and preferences, including the concept of the burden of persuasion. This advancement expands the framework’s applicability in legal reasoning and beyond. A pivotal aspect of this research is the development of the arg2p framework, a robust and versatile environment integrating theoretical advancements in argumentation. The framework marks a significant stride in realising practical, logic-based environments for argumentation in intelligent systems, demonstrating a marked focus on user-friendliness and technical maturity, crucial for bridging theoretical innovation with functional application. The thesis also delves into the realm of machine learning (ML), illustrating the integration of structured argumentation with automated machine learning (AutoML). This integration is aimed at enhancing the transparency and control in the development of ML systems by offering a symbolic interface for incorporating expertise in ML, exemplifying the convergence of traditional symbolic AI methods with data-driven ML approaches. This work significantly contributes to argumentation theory and legal AI, providing a nuanced understanding of meta-argumentation and its practical applications. The enhancements to ASPIC+, coupled with the arg2p framework, present new avenues for legal analysis and decision-making. The integration with ML further highlights the potential of structured argumentation in contemporary AI, paving the way for more robust and ethically sound AI systems across various domains

Burden of persuasion in argumentation: A meta-argumentation approach

This paper examines the view of the burden of persuasion as meta argument and elaborates the meta-argumentative aspects of a burden-of-persuasion semantics in argumentation. An argumentation framework composed of a meta level (dealing with the burden) and an object level (dealing with standard arguments) is proposed and discussed, and its equivalence with the burden-of-persuasion model in argumentation is proved. Finally, a computationally-feasible implementation of the meta-argumentation approach is presented

A Mechanism for Reasoning over Defeasible Preferences in Arg2P

This paper introduces argumentation over defeasible preferences in Arg2P, an argumentation framework based on logic programming. A computational mechanism is first implemented in Arg2P according to Dung’s defeasible preference model, then generalised to enable arbitrary preference relations over arguments

Neuro-symbolic Computation for XAI: Towards a Unified Model

The idea of integrating symbolic and sub-symbolic approaches to make intelligent systems (IS) understandable and explainable is at the core of new fields such as neuro-symbolic computing (NSC). This work lays under the umbrella of NSC, and aims at a twofold objective. First, we present a set of guidelines aimed at building explainable IS, which leverage on logic induction and constraints to integrate symbolic and sub-symbolic approaches. Then, we reify the proposed guidelines into a case study to show their effectiveness and potential, presenting a prototype built on the top of some NSC technologies

Burden of Persuasion in Meta-argumentation

This work defines a burden of persuasion meta-argumentation model interpreting the burden as a set of meta-arguments. Bimodal graphs are exploited to define a meta level (dealing with the burden) and an object level (dealing with standard arguments). Finally, an example in the law domain addressing the problem of burden inversion is discussed in detail

Burden of Persuasion: A Meta-argumentation Approach

This work defines a burden of persuasion meta-argumentation model interpreting burden as a set of meta-arguments. Bimodal graphs are exploited to define a meta level (dealing with the burden) and an object level (dealing with standard arguments). A novel technological reification of the model supporting the burden inversion mechanism is presented and discussed

Large Language Models and Explainable Law: a Hybrid Methodology

The paper advocates for LLMs to enhance the accessibility, usage and explainability of rule-based legal systems, contributing to a democratic and stakeholder-oriented view of legal technology. A methodology is developed to explore the potential use of LLMs for translating the explanations produced by rule-based systems, from high-level programming languages to natural language, allowing all users a fast, clear, and accessible interaction with such technologies. The study continues by building upon these explanations to empower laypeople with the ability to execute complex juridical tasks on their own, using a Chain of Prompts for the autonomous legal comparison of different rule-based inferences, applied to the same factual case