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

Class schedule

The timetable for this course is available here: Portale EasyAcademy