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Artificial Intelligence

Code: CC2006     Acronym: CC2006     Level: 200

Keywords
Classification Keyword
OFICIAL Computer Science

Instance: 2018/2019 - 2S Ícone do Moodle

Active? Yes
Web Page: http://www.dcc.fc.up.pt/~ines/aulas/1819/IA/IA.html
Responsible unit: Department of Computer Science
Course/CS Responsible: First Degree 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 4 Official Study Plan 3 - 6 56 162
L:CC 54 Plano de estudos a partir de 2014 2 - 6 56 162
L:F 0 Official Study Plan 2 - 6 56 162
3
L:G 0 study plan from 2017/18 2 - 6 56 162
3
L:M 3 Official Study Plan 2 - 6 56 162
3
L:Q 0 study plan from 2016/17 3 - 6 56 162
MI:ERS 101 Plano Oficial desde ano letivo 2014 2 - 6 56 162

Teaching Staff - Responsibilities

Teacher Responsibility
Inês de Castro Dutra

Teaching - Hours

Theoretical classes: 2,00
Laboratory Practice: 2,00
Type Teacher Classes Hour
Theoretical classes Totals 1 2,00
Inês de Castro Dutra 2,00
Laboratory Practice Totals 6 12,00
Inês de Castro Dutra 4,00
Pedro Gabriel Dias Ferreira 2,00
Vitor Manuel de Morais Santos Costa 6,00

Teaching language

Suitable for English-speaking students

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 two disciplines: Design and Analysis of Algorithms and 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.

4 . Machine learning: inductive systems. Decision trees, information gain. The WEKA tool. Data Analysis.

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/1819/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 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
then
    APPROVED
else
FAILED

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.

Classification improvement

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

Observations

Desenho e Análise de Algoritmos
Estruturas de Dados
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