=Paper=
{{Paper
|id=Vol-3762/555
|storemode=property
|title=AI for Sustainability: Research at Ud'A Node
|pdfUrl=https://ceur-ws.org/Vol-3762/555.pdf
|volume=Vol-3762
|authors=Gianluca Amato,Alessia Amelio,Luciano Caroprese,Piero Chiacchiaretta,Fabio Fioravanti,Luigi Ippoliti,Maria Chiara Meo,Gianpiero Monaco,Christian Morbidoni,Luca Moscardelli,Maurizio Parton,Francesca Scozzari
|dblpUrl=https://dblp.org/rec/conf/ital-ia/AmatoACCFIMMMMP24
}}
==AI for Sustainability: Research at Ud'A Node==
https://ceur-ws.org/Vol-3762/555.pdf
AI for Sustainability: Research at Ud’A Node
Gianluca Amato1,4,† , Alessia Amelio1,† , Luciano Caroprese1,† , Piero Chiacchiaretta2,3,† ,
Fabio Fioravanti1,4,† , Luigi Ippoliti1,† , Maria Chiara Meo1,4,† , Gianpiero Monaco1,4,† ,
Christian Morbidoni1,† , Luca Moscardelli1,4,*,† , Maurizio Parton1,4,† and Francesca Scozzari1,4,†
1
University of Chieti-Pescara, viale Pindaro 42, Pescara, 65127, Italy
2
University of Chieti-Pescara, via dei Vestini 31, Chieti, 66013, Italy
3
Advanced Computing Core, Center for Advanced Studies and Technology - C.A.S.T., via L. Polacchi 11, Chieti, 66013, Italy
4
Laboratory of Computational Logic and Artificial Intelligence, University of Chieti–Pescara, Pescara, Italy
Abstract
This paper summarizes the activities regarding the development of Artificial Intelligence (AI) for Sustainability conducted
by the members of the AIIS (Artificial Intelligence and Intelligent Systems) node of the University “G. d’Annunzio" of
Chieti-Pescara (Ud’A).
Keywords
Artificial Intelligence, Multi-Agent Systems, Argumentation, Abstraction, Verification, Large Language Models, Machine
Learning, Deep Learning, Sustainability
1. Introduction Existence, Complexity and efficiency of stable so-
lutions in green-Oriented GAMES (ECOGAMES)
There is a growing recognition of AI’s own environmen- funded by PNRR Mission 4, line 1.3, funded by the Euro-
tal footprint and this calls for AI researches being envi- pean Union – NEXTGENERATIONEU, “Future Artificial
ronmentally responsible and aligned with sustainability Intelligence – FAIR” project - PE0000013, Spoke 9, CUP
values. In fact, AI can serve as a powerful instrument for D23C24000210006. The project aims at bridging the fields
addressing environmental and climate issues, optimis- of game theory and environmental sustainability with a
ing decision-making, improving energy efficiency, and specific focus on integrating energy-efficiency in systems
facilitating the transition to a more sustainable future. formed by selfish agents.
Smart Knowledge: Enhancing Argumentation
2. Projects and Abstraction for Explanation and Analysis
(SMARTK) funded by PNRR Mission 4, line 1.3, funded
We are actively participating in several projects, having by the European Union – NEXTGENERATIONEU, “Fu-
AI for sustainability or AI in general as central topic: ture Artificial Intelligence – FAIR” project - PE0000013,
Spoke 9, CUP D23C24000220006. The project aims at
Ital-IA 2024: 4th National Conference on Artificial Intelligence, orga- advancing the fields of argumentation, abstraction, and
nized by CINI, May 29-30, 2024, Naples, Italy
*
Corresponding author. automated reasoning in knowledge representation.
†
These authors contributed equally. Social Interaction with Argumentation (ASIA)
$ gianluca.amato@unich.it (G. Amato); alessia.amelio@unich.it funded by INdAM-GNCS. The ASIA project proposes
(A. Amelio); luciano.caroprese@unich.it (L. Caroprese);
a novel combination of methodologies, techniques, and
piero.chiacchiaretta@unich.it (P. Chiacchiaretta);
fabio.fioravanti@unich.it (F. Fioravanti); luigi.ippoliti@unich.it tools for argumentation analysis, machine learning, and
(L. Ippoliti); mariachiara.meo@unich.it (M. C. Meo); social network analysis.
gianpiero.monaco@unich.it (G. Monaco);
Modeling and Formal Verification of Dialog Sys-
christian.morbidoni@unich.it (C. Morbidoni);
luca.moscardelli@unich.it (L. Moscardelli); tems funded by INdAM-GNCS, CUP E53C22001930001.
maurizio.parton@unich.it (M. Parton); francesca.scozzari@unich.it The project aims to explore existing literature and im-
(F. Scozzari) prove upon the methods for validating abstract argumen-
0000-0002-6214-5198 (G. Amato); 0000-0002-3568-636X tation frameworks that have been previously suggested,
(A. Amelio); 0000-0002-0173-0131 (L. Caroprese);
while also potentially introducing new approaches.
0000-0003-1089-9809 (P. Chiacchiaretta); 0000-0002-1268-782
(F. Fioravanti); 0000-0003-2335-746X (L. Ippoliti); Formal Verification of Debates in Argumen-
0000-0002-3700-3788 (M. C. Meo); 0000-0002-0998-5649 tation Theory funded by INdAM-GNCS, CUP
(G. Monaco); 0000-0003-0244-9322 (C. Morbidoni);
0000-0002-9256-481X (L. Moscardelli); 0000-0003-4905-3544
E55F22000270001. The aim of the project is to extend
(M. Parton); 0000-0002-2105-4855 (F. Scozzari) formal debate verification approaches in argumentation
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
theory using new abstraction and probability-based of the groups. Considering environmental sustainabil-
interpretations. ity, teamwork among different stakeholders (individuals,
Multi-objective Optimization of Digitally Manu- communities, businesses, and governments) is essential
factured Earth Building Components supported by for achieving common goals such as reducing pollution
Neural Networks (MUD-MADE) funded by PNRR and carbon emissions, addressing environmental and cli-
Mission 4 - Component C2, Investment 1.1, PRIN, CUP mate issues or managing natural resources sustainably.
D53D23020070001. The project aims to propose a novel A notable class of coalition formation games is that of
artificial intelligence-supported workflow useful for de- hedonic games, introduced in [3], in which agents have
signing raw earth building components produced with preferences over the set of all possible agent coalitions,
digital manufacturing technology (i.e. 3D printing, and the utility of an agent merely depends on the compo-
Robotic arm or Laser cutter). sition of the coalition she belongs to. Work on hedonic
games mainly studies the existence, computation and
Fracture Risk evaluation in bone metastatic pa- performance of stable solutions, i.e., solutions where no
tients by Artificial InteLligence (FRAIL) funded by agent or group of agents has interest in deviating from
PNRR Mission 4 - Component C2, Investment 1.1, PRIN, the outcome, with respect to several notions of stabil-
CUP D53D23013760006. The project investigates whether ity such as Nash or strong Nash stability, core stability,
AI can produce a reliable and explainable decision sup- individual stability and so on (see [4] for a nice survey).
port system that can assist physicians in complex care Some members of the AIIS node of Ud’A are actively
decisions regarding patients affected by bone metastases, working on coalition formation games [5, 6, 7, 8].
by making their treatment as accurate as possible.
3.3. A Concurrent Language for
3. Research Activities Interacting Argumentative Agents
We now describe the scientific work related to AI for Many AI applications aim to mimic human behavior
Sustainability that is carried out from the researchers and reasoning to allow machines to emulate human-like
of the Ud’A node of AIIS, with particular focus on the thoughts and actions. A significant challenge lies in pro-
projects listed in Section 2. viding mechanisms to formally articulate specific types of
knowledge, allowing machines to use it to reason and in-
fer new insights. Modelling the behaviour of concurrent
3.1. On Green Sustainability of Resource agents that interact and reason in a dynamic environment
Selection Games is a difficult task. It requires tools that can effectively
In our interconnected world, increasingly dependent on capture different types of interactions, such as persuasion
digital platforms and, at the same time, marked by grow- and deliberation, while helping agents make decisions
ing concerns about environmental sustainability, a press- or reach agreements. Argumentation theory provides
ing issue demanding timely and efficient solution is the formal frameworks for representing and evaluating in-
reduction of power consumption of Information Tech- teracting arguments. It is therefore important to define a
nology (IT) devices (personal computers, data centers, language for modeling the interaction of concurrent and
networks). It is highly anticipated, in fact, that their argumentative agents in a distributed system. This lan-
contribution to the annual electricity consumption in guage should allow the representation of different types
2030 will exceed 10% of the total demand [1]. Motivated of dialogues, describing the reasoning process employed
by these considerations, we have studied in [2] a multi- by agents, thus making it a powerful tool for agent inter-
agent system in which agents compete for the usage of action.
power-consuming resources and are charged a cost pro- A member of the AIIS node of Ud’A is actively working on
portional to their fair share of the power consumption, the definition of a Concurrent Language for Interacting
by investigating the (in)efficiency of stable solutions. Argumentative Agents [9, 10, 11, 12].
3.2. Coalition Formation Games 3.4. Abstraction and Explanation
Teamwork and coalition or group formation is an impor- In computer science, abstraction is the standard tool used
tant and widely investigated issue in computer science to model and study complex systems. For example, ab-
research. In many economic, social and political situa- straction is used in model checking, analysis and veri-
tions, individuals carry out activities in groups rather fication of software, neural network analysis or robust
than by themselves. In these scenarios, it is of crucial learning.
importance to consider the satisfaction of the members The AIIS node of Ud’A has a strong background in ab-
straction, in particular on abstract interpretation and its
many applications [13, 14]. In this context, by exploiting provided a time series forecast of NO2 and CO given four
the algorithms [15, 16] and the tools [17, 18] developed bymeteorological parameters: (i) air pressure, (ii) relative
the members of the AIIS node of Ud’A we are designing humidity, (iii) average daily temperature, and (iv) wind
abstractions and tools for the analysis of the concurrent speed [29]. We have also recently proposed a novel re-
language for interacting argumentative agents discussed current neural network-based system for tracking and
in the previous section [9, 10] and for the analysis of forecasting the dispersal of air pollutants PM2.5 on build-
power consumption of software and neural networks. ing sites based on established environmental parameters
[30]. Preliminary findings for predicting pollutants in
3.5. Automated Reasoning for Verification spatial domains can also be found in [31].
This research is conducted in collaboration with the
Several verification problems for programs written in dif- Mathematical Institute of the Serbian Academy of Sci-
ferent programming languages, business processes, net- ences and Arts, the University of East Sarajevo, Italferr
works, and in general software systems, can be modeled S.p.a. and the Atmospheric Physics and Chemistry Labo-
as satisfiability problems for Constrained Horn Clauses ratory (UdAtmo - www.atmo.unich.it).
(CHC) [19]. By exploiting this research, policy-makers and urban
The AIIS node of Ud’A has worked on the development planners can develop more effective strategies for pollu-
of methodologies for transforming CHCs and verifying tion control and urban planning: AI helps in reducing
their satisfiability, with applications in: the health risks associated with air pollution and also
∙ verification of reachability properties for imperative contributes to the broader goal of sustainable urban de-
programs manipulating arrays [20, 21] and Algebraic velopment.
Data Types (e.g. lists and trees) [22, 23, 24];
∙ verification of relational properties among pro-
3.7. Machine Learning Models for
grams [25] (e.g. equivalence, functionality, injectivity,
monotonicity, non-interference); Ecological Footprint Prediction
∙ verification of reachability and controllability proper- In order to comprehend the effects of human activity on
ties of business processes defined using BPMN with time the ecosystem, this research direction studies predictive
extensions [26]; models for ecological footprint, measuring the speed in
∙ generation of CHC verification conditions [27] based consuming resources and generating waste compared
on the operational semantics of the programming lan- to the speed of nature in absorbing human’s waste and
guage and the proof rules of the considered class of prop- generating resources.
erties. In the recent time, we constructed and evaluated four
Focusing on sustainability, we envision potential ap- hybrid machine learning models (artificial neural net-
plications on the verification of power consumption for work, random forest regression and K-nearest neighbor
programs, gas usage for smart contracts on a blockchain, regression) for predicting the total ecological footprint
and on checking relational equivalence of systems, before of consumption from multiple energy inputs and popula-
and after power consumption optimization. tion number. The adopted energy inputs are: (1) natural
gas, (2) coal, (3) oil, (4) wind, (5) solar photovoltaic, and
3.6. Machine Learning to Forecast (6) hydropower sources that are the main sources of en-
Particulate Matter and Trace Gas ergy consumption [32]. We also generated time series
vector autoregression prediction models of the ecological
Emissions for Air Quality Assessment footprint based on energy parameters [33].
This research, focusing on the use of particulate matter The AIIS node of Ud’A is conducting this research with
(PM) data—particularly PM10 and PM2.5 concentrations, the Mathematical Institute of the Serbian Academy of
but also nitrogen oxides (NO𝑥 ) and ozone (O3 )—to eval- Sciences and Arts and with the University of Belgrade.
uate air quality, employs advanced machine learning
techniques including neural network models to analyze 3.8. Social Sustainability and LLMs
and interpret continuous data from regional air quality
monitoring stations [28]. According to the European Green Deal and to the Digital
Furthermore, the integration of meteorological param- Services Act, creating and supporting digital environ-
eters into the machine learning models enhances the ments that are safe and supportive for all individuals is
accuracy of predictions about air quality variations un- one the objectives that underlines the broader sustain-
der specific environmental conditions, demonstrating the able development goals. Combating online misogyny, in
ability of AI to provide deeper insights into environmen- particular, aligns with Sustainable Development Goal 5
tal health impacts. To this respect, using a Nonlinear of the United Nations. While recent advances in Genera-
Autoregressive Exogenous (NARX) neural network, we tive Pretrained Language Models have raised important
questions about their safety and fairness [34], many re- In this research direction, the AIIS node of Ud’A is col-
searchers are exploring their use to enforce online safety. laborating with the Mathematical Institute of the Serbian
Generative LLMs are indeed flexible tools to implement Academy of Sciences and Arts.
data analysis automation task, including hate speech de-
tection [35, 36] and stance/polarity estimation [37]. 3.10. Forecasting Models for Climate
Our experiments in misogynistic content classification
[38] show that a zero-shot GPT 3.5-based classifier out-
Variables and Renewable Energy
performs traditional Deep Learning methods like BERT, Accurately estimating energy production from renew-
without the need for large annotated training sets. Re- able energy sources is crucial for ensuring a reliable and
sults indicate that majority voting among multiple AI consistent supply, aiding in the planning and managing
annotators, each prompted differently, is effective, yet of the power grid. Predicting their power generation
highlights potential for further improvement. Specifi- is notably challenging due to their dynamic behaviour,
cally, we are investigating CoT and Planning-like pat- influenced by factors like time, weather parameters and
terns to improve recognition of more specific classes, as location. Beyond the inherent complexity of these phe-
misogynistic derogation, treatment, or counter-speech. nomena, the difficulties are further compounded by the
In a related line of research, the AIIS node of Ud’A is limited availability of local real-time data, particularly for
collaborating in the RightNets PRIN project, leaded by short-term forecasts. Therefore, the use of forecasting
University of Macerata and University of Sapienza, with models is essential to achieve this goal. In the case of
the goal of enhancing Social Media monitoring dash- missing measured data, Regional Climate Models (RCMs)
boards [39] with AI tools in order to more effectively represent a practical and valuable tool for describing the
monitoring political and electoral debate on social media. climatology of places. The Fifth-Generation Mesoscale
Another research line focuses on utilizing textual data Model (MM5) is one of the most adopted among them.
from social media, particularly during crises and natu- However, various works point out its tendency to under-
ral disasters, to aid disaster responders. However, these /overestimate weather parameters. We proposed DL2 F
studies face challenges such as dealing with unstructured, [43], a powerful deep learning model that combines ex-
noisy, and ambiguous data, varying user credibility, ex- perimental data with MM5 information to address these
pertise, and bias, and the overwhelming volume of data challenges and enhance the accuracy of forecasting pro-
generated during disasters. Leveraging state-of-the-art cedures. Our system forecasts four essential weather
machine learning and AI techniques, we have proposed variables that influence solar power potential: Global
a methodological framework for damage assessment on Horizontal Irradiance (GHI), temperature, atmospheric
tweets related to Hurricane Ida that integrates textual pressure, and relative humidity. The model uses a time
classification of social media data, spatial analysis, and series for each variable as input and the forecast provided
visual analytics to provide rapid responses during natural by the MM5 system. Then, it generates new predictions
disasters [40]. for each variable in the next 3 days. The model’s archi-
tecture is based on a set of GRU (Gated Recurrent Unit)
3.9. Machine Learning Models for neural networks. The new more precise predictions are
then adopted for calculating the electrical energy produc-
Classification of Energy Consumption
tion of a photovoltaic cell [44].
Total energy consumption can be heavily conditioned by
global demographic and economic changes. Accordingly, 3.11. Energy-Efficient Deep Learning
this research direction investigates on prediction models
of energy consumption. Deep learning models, particularly those at the cutting
In the recent time, we considered the application of de- edge, can require significant computational resources
mographic and economic features as predictor variables which translate into high energy consumption. This
for energy consumption. In order to categorize energy not only increases the cost of training and deploying
consumption levels from (i) gross domestic product, (ii) these models but also has a substantial environmental
CO2 emissions, and (iii) total population, we investigated impact due to the carbon footprint associated with en-
the usage of multiclass ensembles of support vector ma- ergy production. In this research direction, we developed
chines and linear discriminant analysis [41]. GloNet [45], an efficient network architecture that can
Also, we applied multiple linear regression and multi- self-regulate its depth, and thus its computational needs,
layer perceptron to forecast energy usage [42]. Energy according to the specific requirements of the task. This
consumption was the dependent variable, whereas the enhances both sustainability and efficiency by reducing
gross domestic product, population as a whole, and CO2 energy consumption and minimizing environmental im-
emissions were considered the predictor factors. pact. Furthermore, GloNet improves the accessibility of
advanced models for users with limited computational
resources, thereby contributing to the democratization of tion problem, J. Artif. Intell. Res. 72 (2021) 1215–
deep learning. This is particularly impactful in resource- 1250.
intensive fields such as reinforcement learning [46, 47], [7] M. Flammini, B. Kodric, G. Monaco, Q. Zhang, Strat-
making them more attainable and broadly usable. egyproof mechanisms for additively separable and
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[8] V. Bilò, G. Monaco, L. Moscardelli, Hedonic games
Some members of the Ud’A AIIS node also work in the with fixed-size coalitions, in: Proceedings of AAAI,
field of health sustainability where we have studied the 2022.
role of AI and the impact of machine learning methodolo- [9] S. Bistarelli, M. C. Meo, C. Taticchi, Timed concur-
gies to analyze the Long COVID syndrome, from clinical rent language for argumentation with maximum
presentation through diagnosis [48]. parallelism, J. Log. Comput. 33 (2023) 712–737.
A multi-omics approach integrating MRI-based ra- [10] S. Bistarelli, C. Taticchi, M. C. Meo, An interleaving
diomics and metabolomics in rectal cancer treatment semantics of the timed concurrent language for ar-
predicts patient responses, potentially reducing unnec- gumentation to model debates and dialogue games,
essary surgeries. A novel machine learning model using Theory Pract. Log. Program. 23 (2023) 1307–1333.
pre-treatment MRI in locally advanced rectal cancer opti- [11] S. Bistarelli, M. C. Meo, C. Taticchi, On the role of
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