Learning objectives
The course aims to introduce students to techniques and technologies designed to reproduce intelligent behaviors on the computer, typical of living beings, with particular attention to knowledge engineering
Knowledge and understanding
In particular, the course aims to illustrate
- the main knowledge representation techniques used in artificial intelligence,
- methodologies for formulating well-defined problems and solutions
- the management of knowledge (certain or uncertain) through logic and reasoning
Application of knowledge and understanding
The main objective is obviously to provide students with the skills to:
- formulate a problem that can be solved by a logical agent
- describe and represent knowledge through the use of logic
- analyze the knowledge present in a domain and choose the method that is considered most appropriate for its management
- solve a real-world problem using one or more AI techniques
Making judgments
To carry out the final project, the student will have to analyze the state of the art in literature to motivate the choices that are made in developing the assignment.
Communication skills
The laboratory exercises and the project can be carried out in small groups, promoting the exchange of opinions. Furthermore, writing the report requires good logical organization and clarity in communicating data and results.
Learning ability
The student's ability to look at things from different perspectives is stimulated by the integration of theory lessons and discussion activities with the students themselves.
Prerequisites
No propedeutic courses. However, students are expected to possess good basic abilities in computer programming and analytical skills.
Course unit content
1 Definitions and history of AI: highlights and crises of AI
2 Formalize the problem and solve problems through research
3 Knowledge representation
4 First order logic
5 Ontologies and descriptive logic
6 Embedding the Knowledge: semantic spaces and knowledge graphs
7 Uncertain knowledge and reasoning
8 Bayesian networks (and Nave bayes classifier)
9 Fuzzy logic
10 Rational decision making in the presence of uncertainty and hints of RL
11 Notes on Reinforcement learning
12 Learning and XAI 6.1 Learning from examples
13 Developing an AI application
14 AI and ethics
Full programme
1 Introduction (4 hours)
1.1 Basic definitions and approaches
1.2 History of AI: highlights and crises of AI
2 Problem formalization and problem solving through research (4 hours)
2.1 Well-defined problems and solutions
2.2 Uninformed or blind search techniques
2.3 Partially informed search techniques
2.4 Informed search strategies (heuristics)
3 Artificial intelligence and games (4 hours)
3.1 Games
3.2 Contradictory research
3.3 Optimal decisions in multiplayer games
4 Knowledge representation (22 hours)
4.1 Logical agents
4.2 First order logic
4.3 Inference in first-order logic
4.4 Knowledge engineering in first-order logic
4.5 Description logic
4.6 Inference in the logic of description
4.7 Ontological engineering
4.8 Embedding the Knowledge: semantic spaces and knowledge graphs
4.8 Uncertain knowledge and reasoning
4.9 Bayesian networks
4.10 Naive Bayes classifier
4.11 Fuzzy logic
4.12 Fuzzy systems
5 Rational decision making in the presence of uncertainty and notes on RL (8 hours)
5.1 decision theory and utility
5.2 Markov processes
5.3 Reinforcement learning
6 Learning and XAI (6 hours)
6.1 Learning from examples
6.2 Learning theory
6.3 Development of an AI application
6.4 XAI definitions
6.5 Moral machine and ethical problems in AI
Bibliography
Russell & Novirg, Artificial Intelligence: A Modern Approach, 3rd Edition. Prentice Hall, 2010
Teaching methods
Lectures and exercises.
Lectures will cover the theoretical aspects of the course subjects.
Practical exercises on real problems will be carried out in class.
Assessment methods and criteria
There is an intermediate test.
The exam consists of two parts:
i) a written test consisting of four open questions on the theoretical topics of the course covered in class with the aim of evaluating the knowledge acquired on these topics.
ii) a written report (and its oral presentation) on a project work that explores one of the topics covered in class
The exam is passed if, in each of the two parts, the student achieves at least a passing grade.
The final grade is a weighted average score obtained in the written test (75%) and that obtained in the project work (25%).
Honors are given if the highest score is achieved on all partial tests.
Other information
Course notes and teaching materials will be distributed during the course in electronic form.
