ARTIFICIAL INTELLIGENCE
Academic Year 2025/2026 - Teacher:
CAROLINA CRESPI
Expected Learning Outcomes
Knowledge and understanding: Students will develop a solid grasp of the implementation and design aspects of diverse algorithmic methodologies for tackling computationally complex and large-scale problems.
Applying knowledge and understanding: Students will acquire the skills required to analyze complex problems and to select and apply suitable solution methodologies based on sound algorithmic reasoning.
Making judgments: Students will be able to assess and identify the most effective and appropriate algorithmic approach for solving complex, large-scale, and real-world problems.
Communication skills: Students will develop the communication abilities and expressive clarity needed to present issues related to intelligent systems and their applications.
Learning skills: Students will be able to transfer acquired knowledge to new contexts and critically appraise the strengths and limitations of various intelligent solution techniques.
Course Structure
The lectures will be held in person and take place in the classroom.
If the course is delivered in blended or distance learning mode, the necessary adjustments may be introduced with respect to what was previously stated, in order to ensure compliance with the program outlined in the Syllabus.
Required Prerequisites
The course assumes a good working knowledge of the programming languages C, C++, Java, Python, and/or MATLAB.
Attendance of Lessons
Attendance is recommended. The lectures help students better grasp the topics covered and the overarching idea linking the various themes, providing useful references and discussions.
Detailed Course Content
The course explores the main computational methodologies for solving complex problems, with a focus on search and optimization strategies. After an introductory overview of problem-solving and state-space search, the course examines the most relevant informed search strategies, such as Greedy Best-First Search, A*, and memory-limited heuristic approaches. It then addresses local search strategies, including Local Search and Hill Climbing, which are particularly effective for large-scale optimization problems. A dedicated section focuses on algorithmic techniques for games and competitive scenarios, such as Minimax and Alpha–Beta pruning, as well as algorithms for constraint satisfaction problems. Finally, the course covers the fundamental exact optimization methods, including Branch-and-Bound and Branch-and-Cut, highlighting their use in the design of efficient and general-purpose computational solutions.
Textbook Information
- Russell, S., & Norvig, P. Artificial Intelligence: A Modern Approach (3rd Edition). Prentice Hall, 2009. ISBN-10: 0136042597 - ISBN-13: 9780136042594
- Talbi, E.-G. Metaheuristics: From Design to Implementation. Wiley, 2009. ISBN-10: 0470278587 - ISBN-13: 9780470278581
- Teaching materials (lecture notes, slides, exercises, articles) provided by the instructor and made available on the instructor’s personal website: https://www.dmi.unict.it/ccrespi/artificial-intelligence/
Course Planning
| | Subjects | Text References |
|---|
| 1 | Informed and uninformed search strategies. | Russell & Norvig, cap. 3 “Solving Problems by Searching” (3.1–3.6) |
| 2 | Local Search Algorithms (Local Search, Hill Climbing, etc.) | Russell & Norvig, cap. 4 “Beyond Classical Search” (4.1–4.2); Talbi, cap. 2 “Single-Solution Based Metaheuristics” (2.1–2.3). |
| 3 | Algorithmic techniques for game theory (Minimax, Alpha-beta, etc.) | Russell & Norvig, cap. 5 “Adversarial Search” (5.1–5.4). |
| 4 | Algorithms for problems with constraints (Map coloring, etc.) | Russell & Norvig, cap. 6 “Constraint Satisfaction Problems” (6.1–6.4); Talbi, cap. 5.3 “Combining Metaheuristics with Constraint Programming”. |
| 5 | Exact methods | Talbi, cap. 1.3.1 “Exact Methods” and cap. 5.2 “Combining Metaheuristics with Mathematical Programming”. |
| 6 | Swarm Intelligence | Talbi, cap. 3.6 “Swarm Intelligence” (3.6.1 Ant Colony Optimization, 3.6.2 Particle Swarm Optimization, 3.7.1 Bees Colony). |
Learning Assessment
Learning Assessment Procedures
Examples of frequently asked questions and / or exercises
ChatGPT ha detto:
Examples of possible projects will be presented during the lectures and made available on the official course webpage. These examples are for illustrative purposes only and may not necessarily correspond to those proposed during the exam.
