Multi-Player Games :
Heuristic search is a broad domain encompassing single-agent, two-player,
and multi-player search. I have made research contributions to a variety
of areas within heuristic search, including applications in commercial
video games, learning, and multi-player games. General areas of research
are described below.
There have been large and well-known successes in writing computer
programs to play two-player games, but the task of playing a game with
more than two players or teams is much more difficult. While research
in two-player zero-sum games has become slightly isolated from general
AI research, the problems faced in multi-player games are common to
many other current domains.
In multi-player games, we cannot escape a need for an opponent model.
If our opponent model is inaccurate, we will need to be able to learn
details of our opponents play. But, the process of search has also proved
to work surprisingly well. Thus, we see a need to not only investigate
search techniques, but also how techniques being develope in areas
such as multi-agent learning can be used effectively on real problems.
My current efforts include work in optimizing search techniques to
minimize tree size, using more advanced opponent models in game trees.
I have investigated learning techniques in the context of multi-player games.
These include techniques both for learning how to play games well, as well as
learning about the strategies of one's opponents.
Pathfinding and Search
Other work on pathfinding
include cooperative pathfinding and research on inconsistent heuristics. I implemented the
pathfinding engine for BioWare on their upcoming game Dragon Age.
Pathfinding is an important task in games and robotics. I will be giving a
tutorial on pathfinding with Sven Koenig and Michael Buro at
(Hierarchical Open Graph) is a framework
which I've written that provides a simple environment for testing and
visualizing algorithms before implementing them in ORTS. HOG is currently
being used by myself and other researchers at the Univeristy of Alberta
as a testbed for multi-agent environments, pathfinding, abstraction
is an Open Real-Time Strategy
game framework. I used to maintain the OS X port.