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

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 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

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

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

Arg-tuProlog: A tuProlog-based argumentation framework

Over the last decades, argumentation has become increasingly central as a frontier research within artificial intelligence (AI), especially around the notions of interpretability and explainability, which are more and more required within AI applications. In this paper we present the first prototype of Arg-tuProlog, a logic-based argumentation tool built on top of the tuProlog system. In particular, Arg-tuProlog enables defeasible reasoning and argumentation, and deals with priorities over rules. It also includes a formal method for dealing with burden of proof (burden of persuasion). Being lightweight and compliant to the requirements for micro-intelligence, Arg-tuProlog is perfectly suited for injecting argumentation into distributed pervasive systems

Periodontal Alteration of the Microcirculation and Hypercholesterolemia: A Possible Correlation?

OBJECTIVE: We evaluated the morphological and parametric characteristics of the periodontal microcirculation in patients diagnosed as having hypercholesterolemia and high levels of low-density lipoprotein (LDL). METHODS: Forty patients were recruited, 20 of whom were affected by hypercholesterolemia and 20 of whom were considered healthy. A videocapillaroscopic examination was carried out on the periodontal mucosa in the proximity of the frenulum (II, V sextant). RESULTS: The difference between the parameters of the hypercholesterolemia group and the control group was evaluated with the Mann-Whitney U-test for non-parametric ordinal data; the level of significance being P < 0.05. The videocapillaroscopy documented extremely significant differences between the two groups, regarding the following parameters: total diameter of the loop (P = 0.0017), diameter of the afferent loops (P = 0.0004), diameter of the efferent loops (P = 0.00008) and periodontal density (P = 0.0001). CONCLUSIONS: The capillaroscopic examination revealed a morphological alteration of the periodontal microcirculation in patients with hypercholesterolemia, which is an expression of peripheral vascular phlogosis

Distribution energy storage investment prioritization with a real coded multi-objective genetic algorithm

Energy Storage Systems (ESSs) are progressively becoming an essential requisite for the upcoming Smart Distribution Systems thanks to the flexibility they introduce in the network operation. A rapid improvement in ESS technology efficiency has been seen, but not yet sufficient to drastically reduce the high investments associated. Thus, optimal planning and management of these devices are crucial to identify specific configurations that can justify ESSs installation. This consideration has motivated a strong interest of the researchers in this field that, however, have separately solved the optimal ESS location and the optimal ESS schedule. In the paper, a novel multi-objective approach is presented, based on the Non-dominated Sorted Genetic Algorithm - II integrated with a real codification that allows joining in a single optimization all the main features of an optimal ESS implementation project: siting, sizing and scheduling. The methodology has been tested on a real-size rural distribution network