Artificial Intelligence

Code:

CC2006

Acronym:

CC2006

Level:

200

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2021/2022 - 2S

Active?	Yes
Web Page:	http://www.dcc.fc.up.pt/~ines/aulas/2122/IA/IA.html
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	0	Official Study Plan	3	-	6	56	162
L:CC	88	study plan from 2021/22	2	-	6	56	162
L:EG	0	The study plan from 2019	3	-	6	56	162
L:F	2	Official Study Plan	2	-	6	56	162
			3
L:G	1	study plan from 2017/18	2	-	6	56	162
			3
L:IACD	2	study plan from 2021/22	2	-	6	56	162
L:M	12	Official Study Plan	2	-	6	56	162
			3
L:Q	0	study plan from 2016/17	3	-	6	56	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 foloowing discipline of Data Structures.

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. 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. POP and CPOP algorithms.

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

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.
Information available at

http://www.dcc.fc.up.pt/~ines/aulas/2122/IA/IA.html

Software

Aleph
WEKA
YAP ou SWI

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Exame	60,00
Trabalho prático ou de projeto	40,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Apresentação/discussão de um trabalho científico	15,00
Estudo autónomo	20,00
Frequência das aulas	28,00
Trabalho escrito	9,00
Trabalho laboratorial	28,00
Total:	100,00

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 with 50% weight each.
          
To be approved, students, in addition to having a positive evaluation, must have a minimum score in the tests: 40% in each test.
 

If the sum T1+T2+Works >= 9.5 and the student meets the minimum grade criteria in the theoretical and practical part of the tests, he is exempt from taking the exam.

Tests are not mandatory. If a student is unable to attend one or both of the tests, he or she can take the exam at the regular time and at the appeal time, if applicable.

Even if the student has taken one of the tests or both, he is also entitled to attend the regular period exam or the appeal period exam.

The exams will be taken in the same model as the tests, with two components:
    1 - material related to the T1 test
    2 - material related to the T2 test

Students can choose one of the components or both. The best grade for each repeated component is chosen.

The final grade will be given by:

N = max(T1,E1) + max(T2,E2) + Jobs
with E1 and E2, the marks for each part of the exam.

If max(T1,E1) >= 40% AND max(T2,E2) >= 40% AND N >= 9.5
then
    APPROVED
if no
   DISAPPROVED

Attention: even with a positive grade, if the student does not obtain a minimum grade in some parts of the tests or exams, he/she will automatically fail.

Classification improvement

Final examination (regular season) or appeal season (Recurso).

Observations

Estruturas de Dados