Layered AI architecture for team based first person shooter video games
View/ Open
Latex Files.zip (25.30Mb)
Date
30/06/2011Author
Graham, Philip Mike
Metadata
Abstract
In this thesis an architecture, similar to subsumption architectures, is presented which
uses low level behaviour modules, based on combinations of machine learning techniques,
to create teams of autonomous agents cooperating via shared plans for interaction.
The purpose of this is to perform effective single plan execution within multiple
scenarios, using a modern team based first person shooter video game as the domain
and visualiser. The main focus is showing that through basic machine learning mechanisms,
applied in a multi-agent setting on sparse data, plans can be executed on game
levels of varying size and shape without sacrificing team goals. It is also shown how
different team members can perform locally sub-optimal operations which contribute
to a globally better strategy by adding exploration data to the machine learning mechanisms.
This contributes to the reinforcement learning problem of exploration versus
exploitation, from a multi-agent perspective.
Collections
The following license files are associated with this item: