An Upper Bound on the Complexity of Tablut

Tablut is a complete-knowledge, deterministic, and asymmetric board game, which has not been solved nor properly studied yet. In this work, its rules and characteristics are presented, then a study on its complexity is reported. An upper bound to its complexity is found eventually by dividing the state-space of the game into subspaces according to specific conditions. This upper bound is comparable to the one found for Draughts, therefore, it would seem that the open challenge of solving this game requires a considerable computational effort.Comment: 9 pages, 1 figur

Symbolic versus sub-symbolic approaches: a case study on training Deep Networks to play Nine Men’s Morris game

Le reti neurali artificiali, grazie alle nuove tecniche di Deep Learning, hanno completamente rivoluzionato il panorama tecnologico degli ultimi anni, dimostrandosi efficaci in svariati compiti di Intelligenza Artificiale e ambiti affini. Sarebbe quindi interessante analizzare in che modo e in quale misura le deep network possano sostituire le IA simboliche. Dopo gli impressionanti risultati ottenuti nel gioco del Go, come caso di studio è stato scelto il gioco del Mulino, un gioco da tavolo largamente diffuso e ampiamente studiato. È stato quindi creato il sistema completamente sub-simbolico Neural Nine Men’s Morris, che sfrutta tre reti neurali per scegliere la mossa migliore. Le reti sono state addestrate su un dataset di più di 1.500.000 coppie (stato del gioco, mossa migliore), creato in base alle scelte di una IA simbolica. Il sistema ha dimostrato di aver imparato le regole del gioco proponendo una mossa valida in più del 99% dei casi di test. Inoltre ha raggiunto un’accuratezza del 39% rispetto al dataset e ha sviluppato una propria strategia di gioco diversa da quella della IA addestratrice, dimostrandosi un giocatore peggiore o migliore a seconda dell’avversario. I risultati ottenuti in questo caso di studio mostrano che, in questo contesto, la chiave del successo nella progettazione di sistemi AI allo stato dell’arte sembra essere un buon bilanciamento tra tecniche simboliche e sub-simboliche, dando più rilevanza a queste ultime, con lo scopo di raggiungere la perfetta integrazione di queste tecnologie

Attention in Natural Language Processing

Attention is an increasingly popular mechanism used in a wide range of neural architectures. The mechanism itself has been realized in a variety of formats. However, because of the fast-paced advances in this domain, a systematic overview of attention is still missing. In this article, we define a unified model for attention architectures in natural language processing, with a focus on those designed to work with vector representations of the textual data. We propose a taxonomy of attention models according to four dimensions: the representation of the input, the compatibility function, the distribution function, and the multiplicity of the input and/or output. We present the examples of how prior information can be exploited in attention models and discuss ongoing research efforts and open challenges in the area, providing the first extensive categorization of the vast body of literature in this exciting domain

An Argumentative Dialogue System for COVID-19 Vaccine Information

open3noDialogue systems are widely used in AI to support timely and interactive communication with users. We propose a general-purpose dialogue system architecture that leverages computational argumentation to perform reasoning and provide consistent and explainable answers. We illustrate the system using a COVID-19 vaccine information case study.openFazzinga, Bettina; Galassi, Andrea; Torroni, PaoloFazzinga, Bettina; Galassi, Andrea; Torroni, Paol

Argumentative Link Prediction using Residual Networks and Multi-Objective Learning.

We explore the use of residual networks for argumentation mining, with an emphasis on link prediction. We propose a domain-agnostic method that makes no assumptions on document or argument structure. We evaluate our method on a challenging dataset consisting of user-generated comments collected from an online platform. Results show that our model outperforms an equivalent deep network and offers results comparable with state-of-the-art methods that rely on domain knowledge

Multi-Task Attentive Residual Networks for Argument Mining

We explore the use of residual networks and neural attention for argument mining and in particular link prediction. The method we propose makes no assumptions on document or argument structure. We propose a residual architecture that exploits attention, multi-task learning, and makes use of ensemble. We evaluate it on a challenging data set consisting of user-generated comments, as well as on two other datasets consisting of scientific publications. On the user-generated content dataset, our model outperforms state-of-the-art methods that rely on domain knowledge. On the scientific literature datasets it achieves results comparable to those yielded by BERT-based approaches but with a much smaller model size.Comment: 12 pages, 2 figures, submitted to IEEE Transactions on Neural Networks and Learning System

A Privacy-Preserving Dialogue System Based on Argumentation

Dialogue systems are a class of increasingly popular AI-based solutions to support timely and interactive communication with users in many domains. Due to the apparent possibility of users disclosing their sensitive data when interacting with such systems, ensuring that the systems follow the relevant laws, regulations, and ethical principles should be of primary concern. In this context, we discuss the main open points regarding these aspects and propose an approach grounded on a computational argumentation framework. Our approach ensures that user data are managed according to data minimization, purpose limitation, and integrity. Moreover, it is endowed with the capability of providing motivations for the system responses to offer transparency and explainability. We illustrate the architecture using as a case study a COVID-19 vaccine information system, discuss its theoretical properties, and evaluate it empirically

Gli Esami non Mentono in CKD-MBD: PTH, Vitamina D & Carbamazepina:

Abstract non disponibil

Deep Networks and Knowledge: from Rule Learning to Neural-Symbolic Argument Mining

Deep Learning has revolutionized the whole discipline of machine learning, heavily impacting fields such as Computer Vision, Natural Language Processing, and other domains concerned with the processing of raw inputs. Nonetheless, Deep Networks are still difficult to interpret, and their inference process is all but transparent. Moreover, there are still challenging tasks for Deep Networks: contexts where the success depends on structured knowledge that can not be easily provided to the networks in a standardized way. We aim to investigate the behavior of Deep Networks, assessing whether they are capable of learning complex concepts such as rules and constraints without explicit information, and then how to improve them by providing such symbolic knowledge in a general and modular way. We start by addressing two tasks: learning the rule of a game and learning to construct the solution to Constraint Satisfaction Problems. We provide the networks only with examples, without encoding any information regarding the task. We observe that the networks are capable of learning to play by the rules and to make feasible assignments in the CSPs. Then, we move to Argument Mining, a complex NLP task which consists of finding the argumentative elements in a document and identifying their relationships. We analyze Neural Attention, a mechanism widely used in NLP to improve networks' performance and interpretability, providing a taxonomy of its implementations. We exploit such a method to train an ensemble of deep residual networks and test them on four different corpora for Argument Mining, reaching or advancing the state of the art in most of the datasets we considered for this study. Finally, we realize the first implementation of neural-symbolic argument mining. We use the Logic Tensor Networks framework to introduce logic rules during the training process and establish that they give a positive contribution under multiple dimensions

Multimodal Argument Mining: A Case Study in Political Debates

We propose a study on multimodal argument mining in the domain of political debates. We collate and extend existing corpora and provide an initial empirical study on multimodal architectures, with a special emphasis on input encoding methods. Our results provide interesting indications about future directions in this important domain