Artificial Intelligence

Code:

CC2006

Acronym:

CC2006

Level:

200

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2025/2026 - 2S

Active?	Yes
Responsible unit:	Department of Computer Science
Course/CS Responsible:	Bachelor in Computer Science

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
L:B	3	Official Study Plan	3	-	6	48	162
L:CC	108	study plan from 2021/22	2	-	6	48	162
L:EG	1	The study plan from 2019	3	-	6	48	162
L:F	0	Official Study Plan	2	-	6	48	162
			3
L:G	0	study plan from 2017/18	2	-	6	48	162
			3
L:IACD	91	study plan from 2021/22	2	-	6	48	162
L:M	7	Official Study Plan	2	-	6	48	162
			3
L:MA	2	Official Study Plan	3	-	6	48	162
L:Q	0	study plan from 2016/17	3	-	6	48	162

Teaching language

Portuguese and english
Obs.: Materials may be made available in English. Classes will be taught in Portuguese.

Objectives

Objectives: Study fundamental concepts and techniques of general use for Artificial Intelligence.

Learning outcomes and competences

skills:
- Capacity for judicious choice of Artificial Intelligence techniques for use in concrete applications,
- Ability to deploy applications based on these techniques.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

It is strongly recommended that the student has attended at least the Data Structures discipline.

Program

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. Adversarial search: minimax and alpha -beta cut, and MCTS.

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. POP algorithms.

4 . Machine learning: inductive systems. Decision trees, information gain. Probabilistic reasoning. Belief networks. Learning belief networks. Hidden Markov models.

5 . Biologically inspired models: neural networks and genetic algorithms.

Mandatory literature

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

Complementary Bibliography

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.

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Exame	70,00
Trabalho prático ou de projeto	30,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Apresentação/discussão de um trabalho científico	15,00
Estudo autónomo	60,00
Frequência das aulas	48,00
Trabalho escrito	11,00
Trabalho laboratorial	28,00
Total:	162,00

Eligibility for exams

All students can attend the final exam. 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

The final grade will be given by:

F = 0.7*E + 0.3*P

F: final grade
E: Exam
Q: Practical work

To be approved, students must have a grade higher than 2.4 out of 6 in the practical work.
To be approved, students must have a score higher than 8 points in the exam.

Special assessment (TE, DA, ...)

In the special season (época especial), the exam counts for the entire evaluation (20 out of 20).

Classification improvement

In the appeal season (Recurso).

Observations

Jury: Francesco Renna, Hélder Oliveira, Rita Ribeiro, Inês Dutra.