07158 - Artificial Intelligence

Learning outcomes

At the end of the course the student will know the principal languages, the modeling techniques and the reasoning methods that are at the base of artificial intelligence. In particular the student will be able to construct systems that exhibit intelligent behaviours, often simulating the behavior of human experts of a specific discipline. Moreover she will be able to model and solve simple constraint and optimization problems by using constraint programming.

Course contents

The course introduces the fundamental principles and methods used in Artificial Intelligence to solve problems, with a special focus on the search in the state space, planning, knowledge representation and reasoning, and on the methods for dealing with uncertain knowledge. The course will include hands-on labs and seminars on selected topics.

Prerequisites: user-level knowledge of a high-level programming language, in order to successfully understand case studies and applications presented during the lessons.

Part I (Module 1)

Module 1. AI Foundations Decision Making and Automated Planning
The first module introduces the foundations of intelligent decision
making and problem solving. Topics include the a broad introduction to Artificial Intelligence, AI paradigms, Trustworthy AI and the European AI Act. The module then covers uninformed and informed search strategies, basics of game playing (Minimax and Alpha-Beta pruning), Constraint Satisfaction Problems, automated planning techniques (STRIPS, GraphPlan, Partial Order Planning and Hierarchical Planning), and nature-inspired optimization methods, including Genetic Algorithms and Swarm Intelligence.
Practical laboratories and project work complement the theoretical
lectures.

Part II (Modules 2, 3):

Module 2. Knowledge Representation and Symbolic Reasoning
The second module focuses on symbolic knowledge representation and reasoning. Students are introduced to First Order Logic, Logic
Programming and Prolog, followed by ontologies, Description Logics, and Knowledge Graphs. The module also covers temporal reasoning, Event Calculus, rule-based systems, Complex Event Processing and Business Process Management. The final part explores how symbolic knowledge can be integrated with Foundation Models through Knowledge Graphs and Retrieval-Augmented Generation (RAG), providing an introduction to modern neuro-symbolic AI systems.

Module 3. Reasoning under Uncertainty
The final module introduces probabilistic reasoning for intelligent
systems operating under uncertainty. Topics include probability theory, Bayesian Networks, Hidden Markov Models and other
probabilistic graphical models, conditional independence, d-
separation, probabilistic inference (exact and approximate), causal
reasoning and uncertainty management. The module concludes by
discussing the role of probabilistic reasoning and uncertainty
estimation in contemporary AI systems, including Foundation Models. 

Readings/Bibliography

A comprehensive list of textbooks is available on the Web site, and it is reported also in the course slides.

Recommended textbook:

• S. J. Russel, P. Norvig, Artificial Intelligence: A modern approach, Prentice Hall, International edition.

Further readings:

• R. J. Brachman, H. J. Levesque, Knowledge Representation and Reasoning, Elsevier, 2004.
• F. Baader, D. Calvanese, D.L. McGuinness, D. Nardi, P.F. Patel-Schneider (editors), The description logic handbook: Theory, implementation, and applications, Cambridge University Press New York, NY, USA, 2007

Teaching methods

Frontal lessons based on slides, with discussion of practical examples and lab activities. Seminars and invited lectures. Autonomous lab activities are encouraged. The lecturers will suggest possible focussed projects. Topics proposed by the students are also welcome.

Assessment methods

The exam aims at assessing the student's knowledge and skills in the course topics and it consists of three independent parts:

• Part I, covering Module 1;
• Part II, covering Modules 2;
• Part III, covering Module 3.

There will be a separate written exam for each part. Each written exam will include exercises and open questions about all the topics presented in the relevant part of the course. The final grade will be the average of [the score achieved in Part I] and [the sum of the scores achieved in Parts II and III].

Teaching tools

Relevant learning material, including course slides, will be made available via virtuale.unibo.it.

The slides will include a comprehensive list of text books and manuals.

Suggestions for further readings, slides and notes about additional topics and exercises will be made available through the web site.

Office hours

See the website of Michela Milano