This course aims to provide a historical and cultural overview of artificial intelligence, illustrate its main application areas and related issues, and present some fundamental topics such as problem-solving techniques modeled in a search space and two-player game problems. Additionally, students will learn the principles of logical reasoning for knowledge-based problem solving. Finally, the course will provide the foundations for solving problems in stochastic environments through Markov decision processes, which form the basis for learning the principles of reinforcement learning.

The skills that students are expected to acquire include problem modeling and the design and development of software systems for solving problems using artificial intelligence techniques.

01. Introduction to AI
 01.01 History and foundations
  01.02 Overview of the problems tackled in AI
  01.03 Main research areas and application fields

02. Intelligent Agents
   02.01 Principal architectures 
  
03 Problem solving
  03.01 State space and related problem solving methods
  03.02 Non-informed and informed search methods
  03.03 Local Sarch
  03.04 Adversarial search: games
  03.05 Exercises
  
04 Logic and reasoning
  04.01 Logical Agents
  04.02 Propositional Logic
  04.03 First-order Logic
  04.04 Knowledge-based systems
  
05 Uncertainty in knowledge representation and reasoning
   05.01 Probabilistic Reasoning
   05.02 Bayesian Network
   05.03 Markov Decision Process
   05.04 Reinforcement Learning

06 Languages for Artificial Intelligence.
    06.01 Prolog: from logic to logic programming,
    06.02 Prolog programs as solvers
    06.03 Design and development of simple Prolog programs for inference and Java for agent reasoning cycle

There are no mandatory prerequisites. However, the Machine Learning course may help in understanding part whole picture provided in this course.

Knowledge and Understanding: The student will have acquired the fundamental knowledge necessary to understand the foundations of artificial intelligence, starting from the concept of intelligent agents and their design through search and optimisation algorithms, logics for knowledge representation, and reinforcement learning algorithms.

Applied Knowledge and Understanding: The student will be able to comprehend the main characteristics of the most common artificial intelligence problems and will know how to select the most appropriate technique for a specific problem from those learned.

Independent Judgment: The student will be able to assess the adequacy and effectiveness of artificial intelligence methodologies and algorithms, with particular reference to problems of search, optimization, and learning under uncertainty and non-uncertainty conditions.

Communication Skills:The student will learn to appropriately explain the main problem-solving techniques, logical reasoning, and reasoning under uncertainty.

Learning Skills: Upon completing the course, the student will have achieved a good level of autonomy in critically understanding study materials related to the main topics of artificial intelligence. This will also enable them to tackle new design scenarios to which they can apply the acquired knowledge.

The teaching material prepared by the lecturer in addition to recommended textbooks (such as for instance slides, lecture notes, exercises, bibliography) and communications from the lecturer specific to the course can be found inside the Moodle platform › blended.uniurb.it

Exercises for student self-assessment are available on the Moodle platform for blended learning.

Teaching

Theory lectures and laboratory exercises, both in presence and online.

Attendance

Although strongly recommended, course attendance is not mandatory.

Course books

S. J. Russel, P. Norvig: "Artificail Intelligence: A modern approach", Prentice Hall, Last  or previous edition.

Assessment

The final exam consists of a written test, of duration of two hours, organized as a set of exercises and open questions choose by all the topics presented in the course. The total marks for the test sum up to 32 points, with a minimum threshold of 18/32 points; below the threshold the test is considered as "failed".

Disability and Specific Learning Disorders (SLD)

Students who have registered their disability certification or SLD certification with the Inclusion and Right to Study Office can request to use conceptual maps (for keywords) during exams.

To this end, it is necessary to send the maps, two weeks before the exam date, to the course instructor, who will verify their compliance with the university guidelines and may request modifications.

Teaching

Materials from theory lectures and laboratory exercises.

Attendance

Same as attending.

Course books

Same as attending.

Assessment

Same as attending.

Disability and Specific Learning Disorders (SLD)

Students who have registered their disability certification or SLD certification with the Inclusion and Right to Study Office can request to use conceptual maps (for keywords) during exams.

To this end, it is necessary to send the maps, two weeks before the exam date, to the course instructor, who will verify their compliance with the university guidelines and may request modifications.