Modules running in the period selected: 7.
Click on the module to see the timetable and course details.
Course | Name | Total credits | Online | Teacher credits | Modules offered by this teacher |
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Master's degree in Artificial intelligence | Explainable AI (2024/2025) | 6 |
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6 | |
Master's degree in Artificial intelligence | AI & Robotics (2024/2025) | 6 | 1 | (Laboratorio) | |
Master's degree in Artificial intelligence | Explainable AI (2023/2024) | 6 |
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3 | |
Master's degree in Computer Engineering for Robotics and Smart Industry
Course partially running
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Mobile robotics (2023/2024) | 6 |
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1 | (Laboratorio) |
Master's degree in Computer Science and Engineering | Knowledge representation (2022/2023) | 6 |
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2 | |
Master's degree in Computer Engineering for Robotics and Smart Industry
Course partially running
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Mobile robotics (2022/2023) | 6 |
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1 | (Laboratorio) |
Bachelor's degree in Computer Science | Programming I [Matricole pari] (2021/2022) | 12 |
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1 | (Laboratorio) |
Di seguito sono elencati gli eventi e gli insegnamenti di Terza Missione collegati al docente:
Topic | Description | Research area |
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Intelligent agents | Design and development of autonomous entities that can sense, model and interact with the environment in which they operate. These area focuses on the interaction and integration of solution technques for several research topics such as automated planning and reasoning, reinforcement learning, statistical learning and reasoning in face of uncertainty. |
Artificial Intelligence
Distributed artificial intelligence |
AI & robotics | Application of AI techniques to increase the autonomy level of robotic systems. This includes the adaptation of algorithms for autonomous planning and reinforcement learning to: i) handle the cyber-physical constraints imposed by robots operating in partially observable and uncertain scenarios; ii) guarantee the reliability and robustness of robotic systems that operate in open environments (e.g., interacting with humans and other robotic systems); iii) facilitate the use of robotic systems in realistic application by proposing novel paradigms of interaction with users (e.g., train a robot to execute a task rather than specify a control program). |
Artificial Intelligence
Planning and scheduling |
Explainable artificial intelligence | The goal of explainable AI (XAI) is to i) explain black-box models; ii) develop AI models which are interpretable by construction. For instance, this involves causal analysis and discovery, logical models of agency (with logic programming) and logical machine learning (with inductive logic programming). XAI helps characterize model accuracy, fairness, transparency and outcomes in AI-powered decision making; moreover this field focuses on methods for improving model and decision interpretation using statistical and graphical tools. |
Artificial Intelligence
Machine learning |
Neurosymbolic planning | Neurosymbolic AI focuses on combining standard data-driven AI (e.g., reinforcement learning) with symbolic approaches (e.g., logic programming and inductive logic programming), in order to enhance the explainability of AI systems (e.g., autonomous agents), their efficacy in human-robot interaction, and foster incremental knowledge acquisition and generalization in planning. |
Artificial Intelligence
Planning and scheduling |
Title | Starting date |
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Development of an efficient and robust motion planning solution for hyper-redundant robotic manipulators | 10/1/22 |
ARS - Autonomous Robotic Surgery | 10/1/17 |
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