ARTIFICIAL INTELLIGENCE
Academic Year 2026/2027 - Teacher: MARIO FRANCESCO PAVONEExpected Learning Outcomes
Knowledge and Understanding: students will understand the fundamental knowledge of Artificial Intelligence, such as intelligent agent paradigm, problem-solving techniques based on search and constraint satisfaction, knowledge representation and automated reasoning methods, reasoning under uncertainty, and automated planning. They will also gain a basic understanding of the main machine learning paradigms, modern systems based on Large Language Models, and the ethical and regulatory issues related to the development and deployment of intelligent systems.
Applying Knowledge and Understanding: students will be able to model and solve problems using search techniques, constraint satisfaction methods, logical and probabilistic reasoning, planning approaches, and machine learning techniques.
Making Judgements: students will be able to critically evaluate the applicability, advantages, and limitations of several Artificial Intelligence techniques for autonomous decision-making processes in a variety of application domains.
Communication Skills: students will learn to effectively describe and discuss the appropriate technical linguistic skills, models, algorithms, and applications of Intelligent Systems and Artificial Intelligence in general.
Learning Skills: students will develop the ability to independently explore new methods and applications of Artificial Intelligence, adapting the acquired knowledge to emerging technologies and practical application contexts.
Course Structure
Lectures in the classrooms. Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.
Required Prerequisites
The course requires a good understanding of discrete and continuous mathematical tools, and an in-depth knowledge of algorithms and problem complexity.
Attendance of Lessons
Attendance is strongly advised to better understand the topics and how they are linked.
Detailed Course Content
The course introduces the basic concepts, principles, models and algorithms of Artificial Intelligence. It will cover classical techniques of knowledge representation, search, reasoning, planning and machine learning, highlighting their role in the modern Intelligent Systems. The course also offers an overview of the latest developments in AI, including architectures based on Large Language Models, intelligent agents and the ethical and regulatory aspects of AI.
Course content:
- Basic Concepts of Artificial Intelligence;
- Intelligent Agents
- Intelligent Problem Solving and Optimization
- Search in Games
- Knowledge Representation and Reasoning
- Uncertainty Reasoning and Decision Making
- Planning and Autonomous Systems
- Introduction to Machine Learning
- Generative AI and Large Language Models
- Responsible AI
Textbook Information
Textbook: Artificial Intelligence: A Modern Approach, 4th Edition, by S. Russell and P. Norvig. Supplementary material will be suggested and/or provided by the lecturer.
Course Planning
| Subjects | Text References | |
|---|---|---|
| 1 | Basic Concepts of Artificial Intelligence | cap 1, 2 |
| 2 | Intelligent Agents | cap 2 |
| 3 | Intelligent Problem Solving and Optimization: constraint satisfaction problems (CSP); CSP heuristics; local search and metaheuristics. | cap. 3, 4, 6 |
| 4 | Search in Games | cap 5 |
| 5 | Knowledge representation and reasoning: propositional and first-order logic; ontologies; knowledge graphs; symbolic and modern AI. | cap 7, 8, 9, 16 |
| 6 | Uncertainty Reasoning and Decision Making: uncertainty in intelligent systems; probability and Bayes’ theorem; Bayesian networks; decision theory; rational decision-making under uncertainty | cap. 12, 13, 16, 17 |
| 7 | Planning and Autonomous Systems: automated planning; goal-based reasoning; STRIPS; planning as search; applications in autonomous systems; connections between classical planning | cap 11; supplementary material. |
| 8 | Introduction to Machine Learning: AI, ML, and data-driven AI; supervised and unsupervised learning; overfitting and underfitting; introduction to Reinforcement Learning; Agent, environment, reward, and policy concepts | cap. 19, 21; supplementary materials. |
| 9 | Generative AI and Large Language Models: foundation models; basic concepts of transformer architectures; LLMs; prompt engineering; RAG; limitations and hallucinations; generative AI impact on contemporary AI systems. | cap. 21, 23, 24; supplementary materials. |
| 10 | Responsible AI: explainability, ethics, and regulation | cap. 27, 28 |
Learning Assessment
Learning Assessment Procedures
The assessment consists of a theory test examination and an oral interview.
The theory test examination is designed to assess the knowledgements and ability to formulate and solve problems using the techniques and algorithms covered during the course.
The oral interview is designed to assess the understanding of the theoretical aspects, and ability to make connections between the topics covered and the proper use of scientific terminology.
The assessment learning can also be carried out electronically, should the conditions require it.
Students with disabilities and/or DSA must contact the teacher, the CInAP representative of the DMI and CInAP well in advance of the exam date to communicate that they intend to take the exam using the appropriate compensatory measures.
To take the final exam, you have to book on the SmartEdu portal. For any technical issues regarding your booking, please contact the Student Service Office.
Examples of frequently asked questions and / or exercises
Examples of simulations relating to questions and exercises will be made available during the course via the link https://www.dmi.unict.it/mpavone/ai.html or any other link provided during the lessons.