OVERVIEW

The focus of this course will be on the use of AI techniques for generating efficient intelligent behavior in games, with a special attention on improving game play experience.

AIMS AND CONTENT

LEARNING OUTCOMES

• Identify tasks that can be tackled using AI techniques.
• Select the appropriate AI technique for the problem under investigation.
• Design and implement efficient and robust AI algorithms for game tasks
• Evaluate performance and test the implemented algorithms

TEACHING METHODS

Lectures and practical sessions

SYLLABUS/CONTENT

01 - Introduction [LINK]

02 - Unity Game Engine [LINK]

03 - Path Finding [LINK]

04 - Steering [LINK]

05 - Influence Maps [LINK]

06 - Tree Search [LINK]

07 - Tic-Tac-Toe [LINK]

08 - Reinforcement Learning [LINK]

09 - Dynamic Scripting [LINK]

10 - Conversational Agents [LINK]

GIT repo for source code: https://bitbucket.org/account/user/elioslab/projects/MS

RECOMMENDED READING/BIBLIOGRAPHY

• Lecture notes
• A. S. Kyaw, «Unity 4. x Game AI Programming». Packt Publishing Ltd.
• Mat Buckland, «Programming Game AI By Example», Jones & Bartlett Learning.
• Ian Millington, and John Funge. «Artificial intelligence for games». CRC Press.
• Steven Rabin «AI Game Programming Wisdom», Vol. 1-4, Charles River Media
• Stuart Russell and Peter Norvig, «Artificial Intelligence: A Modern Approach» (3a edizione), Prentice Hall
• Penny de Byl, «Holistic Game Development with Unity»

TEACHERS AND EXAM BOARD

Exam Board

RICCARDO BERTA (President)

FRANCESCO BELLOTTI

ALESSANDRO DE GLORIA

LESSONS