Instance: 2018/2019 - 2S
Cycles of Study/Courses
Teaching Staff - Responsibilities
Teaching - Hours
Suitable for English-speaking students
Objectives: Study fundamental concepts and techniques of general use for Artificial Intelligence.
Learning outcomes and competences
- Capacity for judicious choice of Artificial Intelligence techniques for use in concrete applications,
- Ability to deploy applications based on these techniques.
Pre-requirements (prior knowledge) and co-requirements (common knowledge)
It is strongly recommended that the student has attended at least the foloowing two disciplines: Design and Analysis of Algorithms and Data Structures.
1. Search techniques : trees and graphs, search algorithms: depth-first , breadth-first, iterative deepening. Informed search algorithms: greedy search, A* and A* with limited memory. Heuristics. Iterative improvement algorithms: hill-climbing and random -restart hill-climbing, simulated annealing. Constraint Satisfaction Problems: arc-consistency methods. Methods forward checking and lookahead. Adversarial search: minimax and alpha -beta cut.
2 . Knowledge-based systems: representation and manipulation of knowledge, propositional and first order logic, situation calculus. Inference in first order logic: backward chaining and forward chaining, resolution, refutation. Deductive systems.
3. Planning and Intelligent Robotics.
4 . Machine learning: inductive systems. Decision trees, information gain. The WEKA tool. Data Analysis.
5 . Biologically inspired models: neural networks and genetic algorithms .
S. Russell, P. Norvig; Artificial Intelligence: A Modern Approach, 3rd ed, Prentice Hall, 2009
Nils Nilsson; Artifical Intelligence: a new synthesis, Morgan Kaufmann Publishers, 1998. ISBN: 1558604677
P. Winston; Artificial Intelligence, 3rd edition, Addison-Wesley, 1992
Teaching methods and learning activities
Theoretical and theoretical-practical lectures.
Practical classes in the lab.
Practical assignments in the lab.
Information available at
YAP ou SWI
Distributed evaluation with final exam
|Trabalho prático ou de projeto
Amount of time allocated to each course unit
|Apresentação/discussão de um trabalho científico
|Frequência das aulas
Eligibility for exams
All students can attend the final exam. The exam has two components (part 1 and part 2). The student needs to achieve minimum score in each part (for more information, please, see the calculation used for the final evaluation).
Calculation formula of final grade
2 tests: T1 and T2 weighing 50% each.
To be approved, the students, in addition to positive assessment, must have a minimum score on the tests: 40% for each test
If the sum T1 + T2 + Assignments >= 9.5 and the student meets the criteria of minimum score, he/she is not required to take the exam..
The tests are not compulsory. If a student is unable to attend one or both tests, he/she must take the Normal exam or the Recurso exam, if necessary.
Even if the student has attended one of the tests or both, he/she is also entitled to attend the examination of the regular season or the examination of the appeal period.
The examination will be made on the same model of the tests, with two components:
1 - topics related to the T1 test
2 - topics related to the T2 test
Students may choose one of the components or both. The best score of each repeated part is chosen.
The final grade is calculated as follows:
N = max(T1,E1) + max(T2,E2) + Assignments
where E1 and E2 are the grades of each part of the examination..
If max(T1,E1) >= 40% AND max(T2,E2) >= 40% AND N >= 9,5
Attention: even with a positive evaluation, if the student does not obtain a minimum score in some parts of the tests or examinations, he/she will be automatically disqualified.
Final examination (regular season) or appeal season (Recurso).
Desenho e Análise de Algoritmos
Estruturas de Dados