Intelligenza Artificiale - Volume 18, issue 1

Authors: Gori, Marco

Article Type: Obituary

DOI: 10.3233/IA-240069

Citation: Intelligenza Artificiale, vol. 18, no. 1, pp. 1-2, 2024

Article Type: Other

Citation: Intelligenza Artificiale, vol. 18, no. 1, pp. 3-3, 2024

Authors: Brunello, Andrea | Croce, Danilo

Article Type: Editorial

Abstract: The 2023 edition of the AIxIA Conference, held in Rome, brought together a large number of researchers and practitioners to discuss the most recent and important advancements in Artificial Intelligence (AI). The conference featured 19 workshops, organized by 77 experts, attracting 248 submissions and resulting in 16 proceedings. This special issue presents extended versions of selected papers initially showcased at these workshops. Each paper underwent rigorous review and represents a diverse array of topics, reflecting the multifaceted nature of the Italian AI community. The topics covered include ethical foundations to symbiotic AI, symbolic knowledge extraction from black-box models, creative influence …prediction using graph theory, AI approaches to multidimensional poverty prediction, an assessment of AI-based supports for informal caregivers, deep learning-based EEG denoising, AI-assisted board-game-based learning, large language models for assessment and feedback in higher education, geometric reasoning in the Traveling Salesperson Problem, defeasible reasoning in weighted knowledge bases, and conditional computation in neural networks. These contributions demonstrate the innovative and interdisciplinary research within the AI community, offering valuable insights and advancing the field. Show more

Keywords: Artificial intelligence, ethical AI, explainable AI, AI for healthcare, AI in education, formal methods, deep learning, geometric reasoning

DOI: 10.3233/IA-240071

Citation: Intelligenza Artificiale, vol. 18, no. 1, pp. 5-8, 2024

Authors: Carnevale, Antonio | Lombardi, Antonio | Lisi, Francesca A.

Article Type: Research Article

Abstract: This paper advocates for a constructivist approach to symbiosis to restore human-centredness in the governance of Symbiotic Artificial Intelligence (SAI). Challenging rigid, deterministic foundational methods warns against the risk of divorcing ethics from mere adherence to moral principles. Instead, it calls for a shift towards a distributed, contextual, relational, and dialectical structure to embody human-centredness. Through an analysis of the SAI landscape and its interplay between social and technological factors, the paper argues for a reconceptualisation of theoretical foundation and human responsibility within the socio-technical perspective. Chapter 2 delves into foundational issues of SAI, questioning the application of biological categories …and proposing patterns of SAI based on definitions of intelligent life. Chapter 3 explores the potential of a constructivist approach, emphasising flexibility and context awareness, and presents a framework for understanding and evaluating SAI systems, components of an evolving methodology. Show more

Keywords: Symbiotic artificial intelligence (SAI), human-centric AI, AI ethics, machine ethics, philosophy of technology

DOI: 10.3233/IA-240034

Citation: Intelligenza Artificiale, vol. 18, no. 1, pp. 9-20, 2024

Authors: Sabbatini, Federico | Calegari, Roberta

Article Type: Research Article

Abstract: Machine learning black boxes, exemplified by deep neural networks, often exhibit challenges in interpretability due to their reliance on complicated relationships involving numerous internal parameters and input features. This lack of transparency from a human perspective renders their predictions untrustworthy, particularly in critical applications. In this paper, we address this issue by introducing the design and implementation of CReEPy, an algorithm for symbolic knowledge extraction based on explainable clustering. Specifically, CReEPy leverages the underlying clustering performed by the ExACT or CREAM algorithms to generate human-interpretable Prolog rules that mimic the behaviour of opaque models. Additionally, we introduce CRASH, an algorithm …for the automated tuning of hyper-parameters required by CReEPy. We present experiments evaluating both the human readability and predictive performance of the proposed knowledge-extraction algorithm, employing existing state-of-the-art techniques as benchmarks for comparison in real-world applications. Show more

Keywords: Explainable clustering, explainable artificial intelligence, symbolic knowledge extraction, PSyKE

DOI: 10.3233/IA-240026

Citation: Intelligenza Artificiale, vol. 18, no. 1, pp. 21-34, 2024

Authors: Alfieri, Francesco | Asprino, Luigi | Lazzari, Nicolas | Presutti, Valentina

Article Type: Research Article

Abstract: Creative influence is responsible for a considerable part of the creative process of an artist and can largely be associated with their social circle. It has been observed that the type and amount of relationships with other fellow artists correlates with the success of an artist. Most of the recent literature has focused on using artefact similarity as a proxy for creative influence between two artists. However, this approach neglects the significance of an artist’s social network. In this work, we rely on an ontology that comprehensively model the relationship between individuals as a Knowledge Graph and we design an …explainable method based on graph theory to predict the influences of an artist given their social network. We evaluate our method on a dataset of relationships between Jazz musicians and achieve accurate results when compared to baselines that rely on the distribution of the data. Our results are aligned with relevant works from the socio-cognitive and psychology fields. We show that our method generalises to resources where information on influence is not directly available and can be used to enrich existing Knowledge Graphs. The code and the ontology developed is shared at https://github.com/n28div/influence_prediction under CC-BY license. Show more

Keywords: Computational creativity, graph theory, knowledge graph, creative influence

DOI: 10.3233/IA-240029

Citation: Intelligenza Artificiale, vol. 18, no. 1, pp. 35-49, 2024

Authors: Olearo, Lorenzo | D’Adda, Fabio | Messina, Enza | Cremaschi, Marco | Bandini, Stefania | Gasparini, Francesca

Article Type: Research Article

Abstract: Despite the rapid development in very recent years of Artificial Intelligence models to predict poverty risk, this problem still remains an unsolved open challenge, especially from a multidimensional perspective. One of the main challenges is related to the scarcity of labelled and high-quality data for training models coupled with the lack of a general reference model to build good predictors. This results in the proposal of a variety of approaches tailored to specific contexts. This paper presents our proposal to address multidimensional poverty prediction, starting from an unlabelled dataset. We focus on the case of a fragile population, the older …adults; our approach is highly flexible and can be easily adapted to various scenarios. Firstly, starting from expert knowledge, we apply a stochastic method for estimating the probability of an individual being poor, and we use this probability to identify three levels of risk. Then, we train an XGBoost classification model and exploit its tree structure to define a ranking of feature relevance. This information is used to create a new set of aggregated features representative of different poverty dimensions. An explainable novel Naive Bayes model is then trained for predicting individuals’ deprivation level in our particular domain. The capacity to identify which variables are predominantly associated with poverty among older adults offers valuable insights for policymakers and decision-makers to address poverty effectively. Show more

Keywords: Multidimensional poverty, poverty prediction, older adults, feature ranking, naive bayes, XGBoost

DOI: 10.3233/IA-240027

Citation: Intelligenza Artificiale, vol. 18, no. 1, pp. 51-65, 2024

Authors: Milella, Frida | Bandini, Stefania

Article Type: Research Article

Abstract: Informal or unpaid caregivers, commonly known as family caregivers, are responsible for providing the 80% of long-term care in Europe, which constitutes a significant portion of health and social care services offered to elderly or disabled individuals. However, the demand for informal care among the elderly is expected to outnumber available supply by 2060. The increasing decline in the caregiver-to-patient ratio is expected to lead to a substantial expansion in the integration of intelligent assistance within general care. The aim of this systematic review was to thoroughly investigate the most recent advancements in AI-enabled technologies, as well as those encompassed …within the broader category of assistive technology (AT), which are designed with the primary or secondary goal to assist informal carers. The review sought to identify the specific needs that these technologies fulfill in the caregiver’s activities related to the care of older individuals, the identification of caregivers’ needs domains that are currently neglected by the existing AI-supporting technologies and ATs, as well as shedding light on the informal caregiver groups that are primarily targeted by those currently available. Three databases (Scopus, IEEE Xplore, ACM Digital Libraries) were searched. The search yielded 1002 articles, with 24 articles that met the inclusion and exclusion criteria. Our results showed that AI-powered technologies significantly facilitate ambient assisted living (AAL) applications, wherein the integration of home sensors serves to improve remote monitoring for informal caregivers. Additionally, AI solutions contribute to improve care coordination between formal and informal caregivers, that could lead to advanced telehealth assistance. However, limited research on assistive technologies like robots and mHealth apps suggests further exploration. Future AI-based solutions and assistive technologies (ATs) may benefit from a more targeted approach to appeasing specific user groups based on their informal care type. Potential areas for future research also include the integration of novel methodological approaches to improve the screening process of conventional systematic reviews through the automation of tasks using AI-powered technologies based on active learning approach. Show more

Keywords: Informal caregiver, artificial intelligence, assistive technology, elderly, supported caring tasks

DOI: 10.3233/IA-240028

Citation: Intelligenza Artificiale, vol. 18, no. 1, pp. 67-87, 2024

Authors: Gabardi, Matteo | Saibene, Aurora | Gasparini, Francesca | Rizzo, Daniele | Stella, Fabio

Article Type: Research Article

Abstract: The artifacts affecting electroencephalographic (EEG) signals may undermine the correct interpretation of neural data that are used in a variety of applications spanning from diagnosis support systems to recreational brain-computer interfaces. Therefore, removing or - at least - reducing the noise content in respect to the actual brain activity data becomes of fundamental importance. However, manual removal of artifacts is not always applicable and appropriate, and sometimes the standard denoising techniques may encounter problems when dealing with noise frequency components overlapping with neural responses. In recent years, deep learning (DL) based denoising strategies have been developed to overcome these …challenges and learn noise-related patterns to better discriminate actual EEG signals from artifact-related data. This study presents a novel DL-based EEG denoising model that leverages the prior knowledge on noise spectral features to adaptively compute optimal convolutional filters for multi-artifact noise removal. The proposed strategy is evaluated on a state-of-the-art benchmark dataset, namely EEGdenoiseNet , and achieves comparable to better performances in respect to other literature works considering both temporal and spectral metrics, providing a unique solution to remove muscle or ocular artifacts without needing a specific training on a particular artifact type. Show more

Keywords: Electroencephalography (EEG), deep learning (DL), frequency-based neural network, EEG denoising, multi-artifact

DOI: 10.3233/IA-240025

Citation: Intelligenza Artificiale, vol. 18, no. 1, pp. 89-102, 2024

Authors: Tinterri, Andrea | Pelizzari, Federica | di Padova, Marilena | Palladino, Francesco | Vignoli, Giordano | Dipace, Anna

Article Type: Research Article

Abstract: Game-Based Learning (GBL) and its subset, Board Game-Based Learning (bGBL), are dynamic pedagogical approaches leveraging the immersive power of games to enrich the learning experience. bGBL is distinguished by its tactile and social dimensions, fostering interactive exploration, collaboration, and strategic thinking; however, its adoption is limited due to lack of preparation by teachers and educators and of pedagogical and instructional frameworks in scientific literature. Artificial intelligence (AI) tools have the potential to automate or assist instructional design, but carry significant open questions, including bias, lack of context sensitivity, privacy issues, and limited evidence. This study investigates ChatGPT as a tool …for selecting board games for educational purposes, testing its reliability, accuracy, and context-sensitivity through comparison with human experts evaluation. Results show high internal consistency, whereas correlation analyses reveal moderate to high agreement with expert ratings. Contextual factors are shown to influence rankings, emphasizing the need to better understand both bGBL expert decision-making processes and AI limitations. This research provides a novel approach to bGBL, provides empirical evidence of the benefits of integrating AI into instructional design, and highlights current challenges and limitations in both AI and bGBL theory, paving the way for more effective and personalized educational experiences. Show more

Keywords: Board game-based learning, artificial intelligence in education, pedagogical frameworks, educational game design

DOI: 10.3233/IA-240030

Citation: Intelligenza Artificiale, vol. 18, no. 1, pp. 103-119, 2024