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Artificial Intelligence for Videogames

Code:

DVAM20

Sigla:

IAV

Áreas Científicas
Classificação	Área Científica
OFICIAL	Ciências Informáticas

Ocorrência: 2023/2024 - 1S

Ativa?	Yes
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	14	Plano_estudos_2018_19	2	-	6	60	162

Língua de trabalho

Portuguese

Objetivos

Provide students with knowledge of problem-solving methods based on Artificial Intelligence techniques.

Provide students with programming skills for state space search algorithms and other algorithms used in game theory.

Resultados de aprendizagem e competências

Understand different artificial intelligence techniques and know their advantages and disadvantages.
Being able to implement AI techniques to solve problems in the area of video games.
Develop group skills and autonomous work skills.

Modo de trabalho

Presencial

Programa

Program Contents

1. Introduction to Artificial Intelligence


2. Search Algorithms




1. Exhaustive Methods


2. Constraint Satisfaction


3. Informed Methods


4. Heuristics-Based Searches




3. Game Theory




1. Games as State-Space Search Problems


2. Minimax Algorithm


3. Alpha-Beta Algorithm




4. Specific AI applications in video game development




1. Intelligent Agents


2. Navigation and wayfinding


3. Procedural Content Generation


4. Genetic Algorithms

Bibliografia Obrigatória

Stuart Russel, Peter Norvig; Artificial Intelligence: A Modern Approach

Bibliografia Complementar

Ian Millington, John Funge; Artificial Intelligence for Games, 2nd edition

Métodos de ensino e atividades de aprendizagem

Regarding theoretical concepts, an expository methodology will be used with analogies to present the various concepts, followed by a participatory methodology with regard to the discussion of the examples presented.

Regarding the practical component, the methodology will be essentially active and participatory using problem solving, analysis of real cases and proposal/development of new solutions.

Tipo de avaliação

Distributed evaluation without final exam

Componentes de Avaliação

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

Componentes de Ocupação

Designation	Tempo (Horas)
Elaboração de projeto	62,00
Estudo autónomo	40,00
Frequência das aulas	60,00
Total:	162,00

Obtenção de frequência

4 mini-projects (game jams) in which they implement:

[15%] Search Algorithms (In particular A*)

[15%] Alpha-Beta Algorithm

[15%] Procedural Generation

[15%] Machine Learning (Reinforcement Learning)


One test, corresponding to 40%.

Fórmula de cálculo da classificação final

MP - Mini Project
T - Test

FINAL = MP1 * 0.15 + MP2 * 0.15 + MP3 * 0.15 +MP4 * 0.15 + T * 0.4