Predicting the Opponent in the Google AI Challenge: Ants
Keywords
No Thumbnail Available
Authors
Issue Date
2013-11-14
Language
en
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Learning to predict the behaviour of an opponent is useful in many tasks, ranging
from computer games to multi-robot situations. This thesis investigates
algorithms for doing so in The Google AI Challenge: Ants, Fall 2011 edition, or
simply called Ants Challenge. The Ants Challenge features a world populated
by artificial ants. Behavioural data of these bots is used by decision tree algorithms
to predict the behaviour of these ants. Literature shows that decision
trees are capable of doing such task. This thesis will test various representations
and algorithms for predicting the opponent. Results of the experiments
show that simple, reactive bots can be predicted with a percentage of correctly
classified instances of up to 98.1%.
Description
Citation
Supervisor
Faculty
Faculteit der Sociale Wetenschappen