Artificial Intelligence
Objectives
The main objective of this course is to provide students with the fundamental concepts of Artificial Intelligence so that they can understand what AI is.
The unit also aims to provide students with skills that allow students who show an interest in AI to continue their studies in postgraduate courses.
General characterization
Code
100101
Credits
6.0
Responsible teacher
Vítor Manuel Pereira Duarte dos Santos
Hours
Weekly - Available soon
Total - Available soon
Teaching language
Portuguese. If there are Erasmus students, classes will be taught in English
Prerequisites
Programming Skills
Bibliography
Russell, Stuart, and Norvig, Peter. Artificial Intelligence: a Modern Approach, 4th. Edition, Prentice Hall, 2020 ;
Elaine Rich, Kevin Knight; Artificial intelligence. ISBN: 0-07-100894-2
Ernesto Costa e Anabela Simões; Inteligência artificial. ISBN: 972-722-269-2 (PT)
Chitta Baral, "Knowledge Representation, Reasoning and Declarative Problem Solving", Cambridge University Press, 2003;
"The Description Logic Handbook: Theory, Implementation and Applications, 2nd Edition", Franz Baader, Diego Calvanese, Deborah L. McGuinness, Daniele Nardi, Peter F. Patel-Schneider, Cambridge University Press, 2007;
Wooldridge, Michael. An introduction to MultiAgent Systems, 2nd. Edition, John Wiley, 2009;
L Sterling and E Shapiro, "The Art of Prolog: Advanced Programming Techniques (Logic Programming) (2nd ed.)", MIT Press, 1994;
I Bratko, "Prolog Programming for Artificial Intelligence (3rd ed.)", Addison-Wesley, 2001
Teaching method
The exercises (practical classes) that are evaluated by a professor ensure students work since the beginning of the semester in these topics. These exercises also provide professors feedback about the students’ status.
The development of the final project (optional) increases their ability to work in a team as well as applying all the skills acquired during the course. This project is presented and discussed face to face, allowing the development of presentation and argumentation skills, as well as the validation of the projects’ originality.
Evaluation method
Assessment:
1st call: test 1 (30%): + Practice (70%)
2nd call: Theoretical exam (100%) or Theoretical exam (50%) + Practice (50%)
Subject matter
The curricular unit is organized in seven Learning Units (LU):
LU1.Overview and brief history of AI
LU2.Knowledge Representation and Reasoning
- Software Agents (Cognitive Agents v. Reactive Agents, BDI model,…)
- An Introduction to Prolog
LU3.Problem Solving
- Agents and search problems
- Blind search and Heuristic search
- Search with opponents (Games)
LU4. Approaches to the problem of learning
- Apprentice agents.
- Conceptual and inductive learning
- Case Based Reasoning
- Brief Introduction to Artificial Neural Networks
LU5.Evolutionary computing
- Brief introduction to Genetic Algorithms
- Artificial Life
- Artificial Immune Systems - AIS
LU6.Distributed AI s
- Software Agent Societies
- Strategies and approaches
LU7.Future of Artificial Intelligence and social/philosophical impacts