- Docente: Andrea Roli
- Credits: 9
- SSD: ING-INF/05
- Language: Italian
- Moduli: Andrea Roli (Modulo 1) Pedro Pablo Gonzalez Perez (Modulo 2)
- Teaching Mode: Traditional lectures (Modulo 1) Traditional lectures (Modulo 2)
- Campus: Cesena
- Corso: Second cycle degree programme (LM) in Computer Engineering (cod. 8200)
Learning outcomes
The course aims at providing students the fundamentals of
Artificial Intelligence (AI). Students will be able to tackle
search problems using classical AI algorithms, such as uninformed
tree-search algorithms, Best First Greedy and A*, as well as
constraint satisfaction and constrained optimization problems by
means of backtracking search, consistency techniques, constraint
programming and metaheuristics. Moreover, students will know the
basics of evolutionary computation, swarm intelligence techniques
and neural networks. As far as knowledge representation and
automatic reasoning is concerned, students will learn how
propositional and first order logics are used in expert systems and
classical planning techniques.
Course contents
- Introduction
- Foundations of AI
- History of AI
- Application domains of AI
- Problem solving
- Solving Problems by Searching
- Non-informed and informed Search and Exploration
- Constraint Satisfaction Problems (complete and incomplete techniques: standard backtracking, constraint propagation techniques, local search)
- Adversarial Search: two players games, games with uncertainty
- Decision support systems and technologies
- Knowledge representation
- Inference in propositional and first order logic
- Reasoning
- Planning
- Introduction to planning and scheduling problems
- Planning and scheduling solving techniques (basics)
- Machine learning
- Reinforcement learning
- Evolutionary computation
- Neural networks
Readings/Bibliography
Russell, Norvig, "Artificial intelligence: A modern approach", Vol.1 and 2 (partially), second edition, Pearson/Prentice Hall
Teaching methods
- Lectures in classroom
- Lectures in lab (AI software tools)
Assessment methods
Written and oral examination.
Teaching tools
- Lecture slides (in English) and web resources
- AI software tools
Links to further information
http://www.lia.deis.unibo.it/~aro/
Office hours
See the website of Andrea Roli
See the website of Pedro Pablo Gonzalez Perez