Unit INTELLIGENT MODELS
- Course
- Informatics
- Study-unit Code
- A002038
- Curriculum
- Cybersecurity
- Teacher
- Valentina Franzoni
- Teachers
-
- Valentina Franzoni
- Hours
- 42 ore - Valentina Franzoni
- CFU
- 6
- Course Regulation
- Coorte 2026
- Offered
- 2026/27
- Learning activities
- Caratterizzante
- Area
- Discipline informatiche
- Sector
- INFO-01/A
- Type of study-unit
- Obbligatorio (Required)
- Type of learning activities
- Attività formativa monodisciplinare
- Language of instruction
- English
- Contents
- 0 Introduction to AI
1 Agent models
2 State space search, planning
3 Multiagent systems and models
4 Reinforcement learning
5 Simulation of complex systems
6 Social Network Analysis - Reference texts
- Testi di riferimento: Appunti del docente disponibili sulla piattaforma www.unistudium.unipg.it
Artificial Intelligence: A Modern Approach,
Stuart Russell and Peter Norvig
Pearson, last edition
Network Science
Albert Lazlo Barabasi (disponibile online)
http://networksciencebook.com/
Reinforcement Learning: An Introduction,
Richard S. Sutton and Andrew G. Barto, Second Edition, MIT Press, 2018
(disponibile online http://incompleteideas.net/book/RLbook2018.pdf ) Lectures notes made available on www.unistudium.unipg.it - Educational objectives
- Expected learning outcomes.
Knowledge oriented goals:
Knowledge of the main technique for modeling AI agent based problem domains
Knowledge of the main techniques for stata space search, uninformed, informed, local search based, automated reasoning and inference based, automated planning.
Reinforcement learning and policy optimization
Knowledge of modeling techniques for AI application domains characterized by complex networks
Ability oriented goals:
Ability to use the acquird knowledge to model, design and implement solutions to real application problems characterized by artificial agents and/or complex networks - Prerequisites
- Prerequisites for a fruitful acquisition of the contents of this course can be summarized in knowledge of computer science fundamentale, including algorithms, basic concepts of computational complexity, language grammars,basic concepts of logics, data structures, databases and concurrent and distributed system.
- Teaching methods
- In class lectures.
Lab sessions.
Discussion of case studies with the class. Continuous assessment and assignments during the semester.
Oral and practical final exam (project).
Materials, details on continuous assessmnt will be updated on http://www.unistudium.unipg.it, students are required to know the syllabus and follow the techaing updates published on Unistudium. - Other information
- Elearning platform for student-lecturer interaction and online forum communication, upload of material and assignments, http://www.unistudium.unipg.it
- Learning verification modality
- The exam consists of an oral exam covering the entire syllabus and the presentation of a project (individual or group) to be presented orally to the instructor.
For students who regularly attend classes, the oral exam—or parts of it—may be replaced by ongoing assessments (exemptions), leaving the project presentation as the final assessment. In-course assessments are designed during the course based on the class’s level and may vary in number, from 2 (a class with a good level, capable of interacting with the instructor and any tutors and of working independently) to 4 (a class with a lower level, which needs to be guided at every step).
The oral exam covers the main topics addressed during the course. The exercises given to candidates consist of theoretical and formal questions on the course topics and the solution of problems involving the analysis and modeling of real-world application domains using the artificial intelligence techniques learned.
The project involves developing a software project, agreed upon with the instructor, on applications of artificial intelligence to real-world problems by applying the techniques presented in the course in greater depth. The project requires a final report and a presentation by the participants, which form an integral part of the evaluation. The purpose of the project is to acquire and demonstrate experience in applying the knowledge gained to a real or simulated problem.
For information on support services for students with disabilities and/or learning disabilities, visit http://www.unipg.it/disabilita-e-dsa - Extended program
- 0-Introduction to AI
Historical, current, and future perspectives on AI and machine learning
1-Agent models, autonomous agents
Reflexive, state-based, inference-based, and learning agents,
2-State space search, planning
State-space search models applied to uninformed and informed search, local search,
adversarial search, automated planning, logic-based action representation, PDDL
3-Multiagent systems and models
Cellular automata, strategy-driven agents, behavioral agents, emerging collective behaviors
4-Reinforcement learning
Markov decision processes, policy evaluation, Q-learning, integrating planning and learning, IRL, RLHU
5-Simulation of complex systems
Basics of complex AI systems, NetLogo
6-Social Network Analysis
Graph theory, random and scale-free networks, Barabási-Albert model, metrics, communities, knowledge networks, social networks, information diffusion, network agents, link prediction - Obiettivi Agenda 2030 per lo sviluppo sostenibile
- Quality Education
Industry , Innovation, Infrastrucures
Sustainable cities and communities