Base Knowledge
Approved in the following curricular units:
– Programação;
– Programação Orientada a Objetos;
– Análise Matemática I e II.
Teaching Methodologies
The course consists of theoretical and practical lectures. Theoretical lectures present methodologies and examples of applications to case studies. The practical lectures focus on the implementation of algorithms and support the solution of practical work.
Students will be assessed on two components, theoretical (12 points) and practical (8 points).
The theoretical component is assessed by a written examination.
The practical component will be assessed by two practical assignments:
– Practical Assignment 1 – Rational Agents (2 marks);
– Practical Assignment 2 – Local Search and Evolutionary Computation (6 marks).
Learning Results
By the end of this course, students should be able to:
– Identify the main paradigms and algorithms of Artificial Intelligence
– Analyze an optimization problem, identify its main characteristics, basic components and apply the correct algorithms –
Recognize the advantages and limitations of the different algorithms for problem solving
– Justify the main options taken during the developments of the algorithms – Develop in an autonomous way new strategies for problem solving
Program
1. Artificial Intelligence – A General Overview
2. Rational Agents and Problem Solving
3. Search Methods
3.1 Non-Informed Search
3.2 Heuristic Search
4. Local Search Techniques
5. Evolutionary Computation
6. AI in Games
7. Machine Learning
Curricular Unit Teachers
Internship(s)
NAO
Bibliography
Main:
– S. Russell e P. Norvig, (2003). Artificial Intelligence: A Modern Approach. Prentice-Hall. (1A-4-125 (ISEC) – 11658).
Complementary:
Ertel, W. (2018). Introduction to artificial intelligence. Springer.
Michalewicz, Z., & Fogel, D. B. (2013). How to solve it: modern heuristics. Springer Science & Business Media.
Costa, E., & Simões, A. (2008). Inteligência artificial: fundamentos e aplicações, 2ª edição, FCA Editora de Informática.
Eiben, A. E., & Smith, J. E. (2003). Introduction to evolutionary computing (Vol. 53, p. 18). Berlin: springer.
Mitchell, T. M., & Learning, M. (1997). The McGraw-Hill Companies. Inc., New York.
Nilsson, N. J., & Nilsson, N. J. (1998). Artificial intelligence: a new synthesis. Morgan Kaufmann.