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Artificial Intelligence

Code:

DVAM12

Sigla:

IA

Áreas Científicas
Classificação	Área Científica
CNAEF	Informatics Sciences

Ocorrência: 2021/2022 - 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	10	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. Whenever possible, the project will be integrated with the contribution of other curricular units of the semester.

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

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

• Participation in classes and completion of tasks (15%)


• Phase 0 - Game Design Document (5%)


• Phase 1 - Single player version (15%)


• Phase 2 – Multiplayer version and final discussion (25%)

Theoretical Component: two Tests (40%) - minimum grade of 8.0 values Test 1 - 20% Test 2 - 20%

Assessment by Exam

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

Theoretical component: one exam - minimum grade of 9.5

Fórmula de cálculo da classificação final

Continuous evaluation:


FINAL GRADE: 40% theoretical component + 60% project component

Approval with an average of the two components >= 9.5 values

Assessment by Exam

50% theoretical component + 50% practical component

Approval with an average of the two components >= 9.5 values

Melhoria de classificação

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