Autenticação

Utilizador:

Senha:

Iniciar sessão

Esqueceu-se da senha?

Artificial Intelligence

Code:

DVAM12

Sigla:

IA

Áreas Científicas
Classificação	Área Científica
CNAEF	Informatics Sciences

Ocorrência: 2022/2023 - 1S

Ativa?	Yes
Página Web:	https://moodle.ips.pt/2122/course/view.php?id=490
Unidade Responsável:	Departamento de Sistemas e Informática
Curso/CE Responsável:	Professional Technical Higher Education Course in Development of Videogames and Multimedia Applications

Ciclos de Estudo/Cursos

Sigla	Nº de Estudantes	Plano de Estudos	Anos Curriculares	Créditos UCN	Créditos ECTS	Horas de Contacto	Horas Totais
DVAM	16	Plano_estudos_2018_19	2	-	6	60	162

Língua de trabalho

Portuguese

Objetivos

This course aims to introduce the area of artificial intelligence to students and how it can be applied to game development. 

Resultados de aprendizagem e competências

After completing the course, students should:
1- Understand what AI is and what problems it can apply to
2- Know how to analyze a problem and identify AI techniques that can be applied
3- Know what it takes to build an AI
4- Understand the role of AI in games
5- Know some of the simplest AI techniques, where and how they can be applied and what are their advantages and limitations
6- Being able to implement AI techniques for simple game-themed problems

Modo de trabalho

Presencial

Programa

1. Introduction to Artificial Intelligence
2. Uninformed search:

• Width, depth, uniform cost

3. Informed search: Greedy, A*
4. Min Max: Search with opponents

• Alpha Beta prunning


• Hidden information and randomness

5. Other AI topics: Constraint satisfaction, logic, etc.

Bibliografia Obrigatória

Stuart Russell and Peter Norvig; Artificial Intelligence: A Modern Approach, 4thEdition, Prentice-Hall, 2020
Millington, I., Funge, J.; Artificial Intelligence for Games (2nd ed.), CRC, 2009

Bibliografia Complementar

Buckland, M.; Programming Game AI by Example, Jones & Bartlett Learning., 2004

Métodos de ensino e atividades de aprendizagem

An expository methodology will be adopted to introduce the various topics. This will be reinforced with an experimental practice methodology, through practical tutorial exercises and a final project.

Tipo de avaliação

Distributed evaluation without final exam

Componentes de Avaliação

Designation	Peso (%)
Teste	40,00
Trabalho escrito	60,00
Total:	100,00

Componentes de Ocupação

Designation	Tempo (Horas)
Elaboração de projeto	40,00
Frequência das aulas	60,00
Trabalho laboratorial	8,00
Total:	108,00

Obtenção de frequência

Aprovment in all components that are referred in the section regarding evaluation calculation.

Fórmula de cálculo da classificação final

The assessment consists of two components: theoretical and practical. Both are mandatory. The minimum grade is 9,5 out of 20 for the average of both components.

Continuous evaluation:

Practical Component: one Project (60%) - minimum grade of 8.0 values

Theoretical Component: one Test (40%) - minimum grade of 8.0 values 

Assessment by Exam

Practical Component: Delivery of the final version of the project and final discussion (50%) (Min. 8.o points)

Theoretical component: one exam (50%) - minimum grade of 8.0

Melhoria de classificação

It is only possible to improve the theoretical component, only in the exam in the second call.