Machine learning has proven useful for producing solutions to various
problems, including the creation of controllers for autonomous intelligent
However, the control requirements for an intelligent agent sometimes go
beyond the simple ability to complete a task, or even to complete it
efficiently: An agent must sometimes complete a task in style
For example, if an autonomous intelligent agent is embedded in a game
where it is visible to human observers, and plays a role that evokes
human intuitions about how that role should be fulfilled, then the agent
must fulfill that role in a manner that does not dispel the illusion
of intelligence for the observers.
Such visibly intelligent behavior
is a subset of general intelligent
behavior: a subset that we must be able to provide if our methods are
to be adopted by the developers of games and simulators.
This dissertation continues the tradition of using neuroevolution to
train artificial neural networks as controllers for agents embedded in
strategy games or simulators, expanding that work to address selected
issues of visibly intelligent behavior.
A test environment is created and used to demonstrate that modified
methods can create desirable behavioral traits such as flexibility,
consistency, and adherence to a doctrine, and suppress undesirable traits
such as seemingly erratic behavior and excessive predictability.
These methods are designed to expand a program of work leading toward adoption
of neuroevolution by the commercial gaming industry, increasing player
satisfaction with their products, and perhaps helping to set AI forward as
The Next Big Thing in that industry.
As the capabilities of research-grade machine learning converge with
the needs of the commercial gaming industry, work of this sort can be expected
to expand into a broad and productive area of research into the nature of
intelligence and the behavior of autonomous agents.
PhD Thesis, Department of Computer Sciences, The University of Texas at Austin. Technical Report AI-06-334.