From Traditional AI to the Future of Agentic AI and Robotics
Luís Paulo Reis, University of Porto, Portugal
Federated Learning (FED) of eXplainable Artificial Intelligence (XAI) Models
Pietro Ducange, University of Pisa, Italy
From Traditional AI to the Future of Agentic AI and Robotics
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Brief Bio
Luis Paulo Reis is an Associate Professor with Habilitation at the University of Porto in Portugal and Director of LIACC – Artificial Intelligence and Computer Science Laboratory. He is an IEEE Senior Member and he was president of the Portuguese Society for Robotics and of the Portuguese Association for Artificial Intelligence. He is Co-Director of LIACD - First Degree in Artificial Intelligence and Data Science. During the last 25 years, he has lectured courses at the university on Artificial Intelligence, Intelligent Robotics, Multi-Agent Systems, Simulation and Modelling, Games and Interaction, Educational/Serious Games and Computer Programming. He was the principal investigator of more than 20 research projects in those areas. He won more than 60 scientific awards including winning more than 15 RoboCup international competitions (including the last 3 editions of the Simulation 3D League - Humanoid Robots) and best papers at conferences such as ICEIS, Robotica, IEEE ICARSC and ICAART. He supervised 24 PhD and 160 MSc theses to completion and is supervising 12 PhD theses. He evaluated more than 50 projects and proposals for FP6, FP7, Horizon2020/Europe, FCT, and ANI. He was a plenary speaker at several international conferences, such as ICAART, ICINCO, LARS/SBR, WAF, IcSports, SYROCO, CLAWAR, WCQR, ECIAIR, DATA/DELTA, IC3K and ICMarkTech. He organized more than 70 international scientific events and belonged to the program committee of more than 300 scientific events. He is the author of more than 500 publications in international conferences and journals.
Abstract
This talk analyzes the evolution of AI from symbolic AI to machine learning and the shift from rule-based expert systems to data-driven approaches, the limitations of traditional AI and why machine learning emerged, the role of data in AI, and the rise of deep learning, deep reinforcement learning, large language models (LLMs) and generative AI. It will then analyze agentic AI as the next frontier, moving from predictive models to autonomous, goal-driven AI agents and from the early examples of agents and agentic AI in automation, research, and self-improving systems to the new generation of agents powered by strong LLMs. It will then analyze Deep Reinforcement Learning (DRL) and how DRL enables AI to learn from trial and error without being data-driven with applications in robotics, autonomous systems, and gaming. Then it will focus on the emergence of Large Behavior Models (LBMs), AI models trained on large-scale behavior data, moving beyond text to multimodal inputs (vision, actions, speech) with applications in robotics, industrial automation, and human-AI collaboration. It will conclude with a deep analysis of the future of robotics and AI integration with the shift from reactive robots to proactive and adaptive intelligent robots, combining LLMs, DRL, and LBMs to create more autonomous robotic systems. Will businesses and society be prepared for this AI next generation? What should we do to prepare ourselves?
Federated Learning (FED) of eXplainable Artificial Intelligence (XAI) Models
Pietro Ducange
University of Pisa
Italy
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Brief Bio
Pietro Ducange received the M.Sc. degree in Computer Engineering and the Ph.D. degree in Information Engineering from the University of Pisa in 2005 and 2009, respectively. Currently, he is an associate professor of Information Systems and Technologies at the University of Pisa, Italy. He teaches Large Scale and Multi Structured Databases, Intelligent Systems and Big Data Management.
He is a senior member of the AI-R&D research Group at the Department of Information Engineering. Moreover, he is a member of the Big Data, Cloud Computing and Cybersecurity Lab and of the Trustworthy and Embodied Intelligence Lab of the same department. His main research interests include explainable artificial intelligence, big data mining, social sensing and sentiment analysis. He has been involved in several R&D projects in which data mining and computation intelligence algorithms have been successfully employed. He has co-authored over 100 papers in international journals and conference proceedings. He organized several workshops, special sessions, tutorials and special issues on Trustworthy AI and its application in engineering fields.
Abstract
The current era is characterized by an increasing pervasiveness of applications and services based on data processing and often built on Artificial Intelligence (AI) and, in particular, Machine Learning (ML) algorithms. In fact, extracting insights from data is so common in daily life of individuals, companies, and public entities and so relevant for the market players, to become an important matter of interest for institutional organizations. The topic is so important and hot that ad hoc guidelines and regulations for designing trustworthy AI-based applications have also been proposed by the European Union and other national and supra-national bodies. One important aspect is given by the capability of the applications to tackle the data privacy issue. Additionally, depending on the specific application field, paramount importance is given to the possibility for the humans to understand why a certain AI/ML-based application is providing that specific output.
Trustworthy AI models should be trained with the simultaneous goals of preserving the data privacy and ensuring a certain level of explainabilty of the system. In this talk, we discuss the concept of Federated Learning (FL) of eXplainable AI (XAI) models, in short FED-XAI, purposely designed to address the two requirements simultaneously.
We first introduce the motivations at the foundation of FL and XAI, along with their basic concepts. Then, we provide a brief survey regarding approaches, models, results, issues and applications on FED-XAI. Finally, we also show our recently released framework for providing user-friendly support to Federated Learning (FL) of Fuzzy Rule-Based Systems (FRBS) as explainable-by-design models.