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. 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