Checking date: 29/04/2019

Course: 2020/2021

Artificial intelligence in the entertainment industry
Study: Bachelor in Computer Science and Engineering (218)

Coordinating teacher: FERNANDEZ ARREGUI, SUSANA

Department assigned to the subject: Department of Computer Science and Engineering

Type: Electives
ECTS Credits: 6.0 ECTS


Students are expected to have completed
Artificial Intelligence
- Ability to solve problems, both individually and in a team (PO a,b,c,d,e,k) - Work in teams to analyze and design computer solutions (PO a,b,c,d) - Ability to analyze and synthesize (PO a,b,c) - Ability of organization and planning (PO b,c,d) - Ability of information management (information acquisition and analysis) (PO a,b,k) - Ability to make decisions (PO a,b,c,d,e) - Motivation for quality and continuous improvement (PO b) - Oral and written communication (PO g) - Critical reasoning (PO a,b,d) - Ability to learn the basics, paradigms and techniques of intelligent systems and analyze, design and build systems, services and applications that use these techniques in any scope (CECC4). Basic knowledge on artificial intelligence applied to entertainment industry (PO a) - Ability to interpret functional specifications towards the development of applications for entertainment industry (PO a,b,c,e) - Ability to acquire, obtain, formalize and represent human knowledge in a computable form for problem solving by a computer system at any scope, particularly those related to aspects of computing, perception and action in smart environments (CECC5). Ability to perform detailed analysis and design of computer applications for entertainment industry (PO a,b,c,e,k)
Description of contents: programme
1. Introduction: IA in the entertainment industry 2. Non Playing Characters (NPCs) programming 2.2 Path finding 2.2.1. path finding: basic techniques 2.2.2. path finding: advanced techniques 2.3 Board games 2.3 Decision Making 2.3.1. Introduction to programming techniques 2.3.2. Finite state machines 2.3.3. Decision trees and rule-based systems 2.3.4. Goal-oriented behaviour 2.4. Tactic and strategy 2.5. Machine Learning 2.6. Collective movement 3. Interfaces and design
Learning activities and methodology
Lectures (1 ECTS) Oriented, among others, towards the competences related to knowledge of concepts, relations among them, techniques to be used, or ways to analyze and synthesize knowledge (PO a, CECC4) Practice (3 ECTS) Oriented, among others, towards the competences related to work in teams, problem solving, work organization, or oral (presentation in public of projects or homeworks) and written communication (written reports on their homeworks and projects) (PO b,c,d,e,g,k, CECC5) Individual work (2 ECTS) Oriented, among others, towards the competences related to planning, analysis, synthesis, critic reasoning, or concept acquisition (PO a,c,e,g, CECC4)
Assessment System
  • % end-of-term-examination 40
  • % of continuous assessment (assigments, laboratory, practicals...) 60
Basic Bibliography
  • David M. Bourg, Glenn Seemann. AI for game developers. O'Reilly. 2004
  • Ian Millington, John Funge. Artificial Intelligence for Games. Morgan Kaufmann. 2009
  • Steve Rabin. AI game programming wisdom (1,2,3,4). Charles River Media. 2002-2008
Additional Bibliography
  • Mat Buckland. Programming Game AI by Example. Wordware Pub. 2004
  • Brian Schwab. AI game engine programming. Course Technology. 2008
  • S. Russell, P. Norvig. Artificial Intelligence: A Modern Approach. Prentice Hall. 2010

The course syllabus and the academic weekly planning may change due academic events or other reasons.