Guía Docente 2020-21
INTELIGENCIA ARTIFICIAL APLICADA A VIDEOJUEGOS

BASIC DETAILS:

Subject: INTELIGENCIA ARTIFICIAL APLICADA A VIDEOJUEGOS
Id.: 31390
Programme: GRADUADO EN DISEÑO Y DESARROLLO DE VIDEOJUEGOS. 2013 (BOE 28/03/2014)
Module: PROGRAMACIÓN DE VIDEOJUEGOS
Subject type: OPTATIVA
Year: 4 Teaching period: Segundo Cuatrimestre
Credits: 6 Total hours: 150
Classroom activities: 64 Individual study: 86
Main teaching language: Inglés Secondary teaching language: Castellano
Lecturer: Email:

PRESENTATION:

Artificial Intelligence (AI) has seen immense progress in recent years. It is both, a thriving research field featuring an increasing number of important research areas, and a core technology for an increasing number of application areas.  Artificial Intelligence is widely regarded in the computer games industry as the area where the most advances will be made in the coming decades. As well as equipping students for a career in the rapidly growing game industry, this couse will lead students to gain knowledge and skills in AI techniques that apply to other domains such as business planning and engineering. This course digs into the application of Artificial Intelligence to Games—focusing on core techniques, essential skills and principles transferable from one domain to another.The course explains the basic role of Artificial Intelligence (AI) in video games. The course shows how AI moves the story and its characters forward and shows how game programs can learn responses and generate plans and movements based on players’ actions. It covers algorithms and languages that enable AI. These ideas are applied using Unity video game engine

The most important outcomes are:

PROFESSIONAL COMPETENCES ACQUIRED IN THE SUBJECT:

General programme competences G01 Ability to use learning strategies independently for use in the continuous improvement of professional practice.
G02 Ability to analyse and synthesise problems of their professional activity and apply in similar environments.
G07 Ability to handle different complex knowledge models through a process of abstraction and its application to approach and solve problems.
Specific programme competences E03 Ability to develop the use and programming of computers, operating systems, databases and software and their application in the development of video games.
E04 Ability to understand and master the basic concepts of discrete logic, algorithmic mathematical and computational complexity, and their application for solving engineering problems.
E05 Ability to program applications both correctly, and efficiently, choosing the most appropriate paradigm and programming languages, applying knowledge of basic algorithmic procedures and using the types and structures of the most appropriate data.
E14 Ability to apply the main foundations and techniques of the smart systems and their practical application in diverse environments.
Learning outcomes R01 Explain the artificial intelligence paradigms most used in video games. .
R02 Apply methods and techniques of artificial intelligence to video games.
R03 Evaluate different artificial intelligence techniques applied to video games.
R04 Propose advanced alternatives to the basic techniques of artificial intelligence in video games.

PRE-REQUISITES:

It is recommended to have studied all subjects in previous semesters especially Intelligent Systems . Basic knowledge will be required in fields like AI, design, programming, object oriented programming, UML, version control...

Unity will be used as the main development platform, previous experience with Unity is not required, but it will help understanding the examples provided. Any other game engine can be used to develop any of the projects requested, but example templates for the projects will be delivered as Unity projects.

 

 

SUBJECT PROGRAMME:

Subject contents:

1 - Introduction to Artificial Intelligence in Videogames
    1.1 - History
    1.2 - Examples
2 - Planning
    2.1 - Search Algorithms
    2.2 - Pathfinding
3 - Decision Making
    3.1 - Decision Trees and State Machines
    3.2 - Behaviour Trees
    3.3 - Scheduling
    3.4 - Autonomous Movement
4 - Machine Learning
    4.1 - State of the Art
    4.2 - Machine Learning in Videogames
5 - Final project

Subject planning could be modified due unforeseen circumstances (group performance, availability of resources, changes to academic calendar etc.) and should not, therefore, be considered to be definitive.


TEACHING AND LEARNING METHODOLOGIES AND ACTIVITIES:

Teaching and learning methodologies and activities applied:

Student work load:

Teaching mode Teaching methods Estimated hours
Classroom activities
Master classes 15
Practical exercises 15
Workshops 15
Laboratory practice 15
Assessment activities 4
Individual study
Tutorials 8
Individual study 20
Individual coursework preparation 20
Project work 25
Research work 3
Compulsory reading 5
Recommended reading 5
Total hours: 150

ASSESSMENT SCHEME:

Calculation of final mark:

Written tests: 20 %
Individual coursework: 20 %
Group coursework: 20 %
Final exam: 30 %
Involvement: 10 %
TOTAL 100 %

*Las observaciones específicas sobre el sistema de evaluación serán comunicadas por escrito a los alumnos al inicio de la materia.

BIBLIOGRAPHY AND DOCUMENTATION:

Basic bibliography:

Russell, Stuart, and Peter Norvig. "AI a modern approach." Learning 2.3 (2005): 4.
YANNAKAKIS, Georgios N.; TOGELIUS, Julian. Artificial Intelligence and Games (First Public Draft). 2017.

Recommended bibliography:

DÍAZ, Guillermo; IGLESIAS, Andrés. Swarm Intelligence Scheme for Pathfinding and Action Planning of Non-player Characters on a Last-Generation Video Game. En International Conference on Harmony Search Algorithm. Springer, Singapore, 2017. p. 343-353.
HASSABIS, Demis. Artificial Intelligence: Chess match of the century. Nature, 2017, vol. 544, no 7651, p. 413-414.
SHAKER, Noor; TOGELIUS, Julian; NELSON, Mark J. Procedural Content Generation in Games. Springer International Publishing, 2016.
SAFADI, Firas; FONTENEAU, Raphael; ERNST, Damien. Artificial intelligence in video games: Towards a unified framework. International Journal of Computer Games Technology, 2015, vol. 2015, p. 5.
CHAMPANDARD, Alex J. AI Game Development: Synthetic Creatures with Learning and Reactive Behaviors. New Riders. 2003.

Recommended websites:

Gamasutra http://gamasutra.com/
GameDev http://www.gamedev.net/
Unity https://unity3d.com
Unreal Engine https://www.unrealengine.com


* Guía Docente sujeta a modificaciones