====== Publications ======


===== Papers =====

Robin Baumgarten and Simon Colton\\
//"Case-based Player Simulation for the Commercial Strategy Game DEFCON"// {{track>:uni:baumgarten_colton_cgames2007.pdf|PDF||87802}}\\
In the Proceedings of CGames, 2007. \\
++++ Abstract |
DEFCON is a nuclear war simulation strategy game released
in 2006 by Introversion Software Ltd. We describe an
approach to simulating human game-play using a variety of
AI techniques, including simulated annealing, decision tree
learning and case-based reasoning. We have implemented an
AI-bot that uses a novel approach to planning fleet
movements and attacks. This retrieves plans from a case base
of previous games, then merges these using a method based
on decision tree learning. In addition, we have implemented
more sophisticated control over low-level actions such as
firing missiles and guiding planes. In particular, we have
written routines to enable the AI-bot to synchronise bombing
runs, and enabled a simulated annealing approach to
assigning bombing targets to planes and opponent cities to
missiles. We describe how our AI-bot operates, and the
experimentation we have performed in order to determine an
optimal configuration for it. With this configuration, our AIbot
beat Introversion’s finite state machine automated player
in 76.7% of 150 matches played.
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Robin Baumgarten, Maria Nika, Jeremy Gow and Simon Colton\\
//"Towards the automatic invention of simple mixed reality games"// {{track>:uni:physgames-aisb09.pdf|PDF||87802}}\\
In the Proceedings of AISB'09 Symposium: AI & Games, 2009. \\
++++ Abstract |
The invention of mixed reality games that combine virtual and
physical play offers a rich and challenging application area for AI
techniques.  We look at the possibility of using descriptive machine
learning to automatically invent simple mixed reality games.
Specifically, we demonstrate that the HR learning system can
generate coherent domain knowledge from the noisy play data gathered
from a number of simple physical games. We describe how this could
be used to support mixed reality game invention, and discuss the
prospects for further work in this area.
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Chong-U Lim, Robin Baumgarten and Simon Colton\\
//"Evolving Behaviour Trees for the Commercial Game DEFCON"// 
{{http://www.doc.ic.ac.uk/~sgc/papers/lim_evogames10.pdf|PDF}}\\
In the Proceedings of EvoStar 2010, EvoGAMES track, Springer. \\
++++ Abstract |
Behaviour trees provide the possibility of improving on existing
Artificial Intelligence techniques in games by being simple to implement,
scalable, able to handle the complexity of games, and modular
to improve reusability. This ultimately improves the development process
for designing automated game players. We cover here the use of
behaviour trees to design and develop an AI-controlled player for the
commercial real-time strategy game DEFCON. In particular, we evolved
behaviour trees to develop a competitive player which was able to outperform
the game’s original AI-bot more than 50% of the time. We aim
to highlight the potential for evolving behaviour trees as a practical approach
to developing AI-bots in games.
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Robin Baumgarten\\
//"Towards Automatic Player Behaviour Characterisation
using Multiclass LDA"// 
{{track>:uni:pacman_survey_aisb.pdf|PDF||87802}}\\
In the Proceedings of AISB'10 Symposium: AI & Games, 2010. \\
++++ Abstract |
The automated classification of player behaviour and a
subsequent adaptation of game rules in video games is an area of
research of interest to both the games industry and to academia, requiring
a combination of AI, HCI, psychology, statistics, data mining
and machine learning. In this paper, we describe an experiment to answer
the question whether it is possible to measure and distinguish
behaviour of a player playing a simple game with very limited inputs
and a limited variety of possible actions. We use the arcade style
action game PacMan to record a variety of game-related metrics in
an online survey comprised of 245 players, each playing 5 sessions
of PacMan. Then, the resulting data is analysed using discretization
methods and linear discriminant analysis. The results indicate
that differentiation between different players and different playing
styles is possible, and that the methods used are suitable to automatically
identify the most significant distinguishing features of player
behaviour.
++++

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Julian Togelius, Sergey Karakovskiy and Robin Baumgarten\\
//"The 2009 Mario AI Competition"// {{track>:uni:mariocompetition.pdf|PDF||87802}}\\
In the Proceedings of the IEEE Congress on Evolutionary Computation (IEEE CEC), 2010. \\
++++ Abstract |
This paper describes the 2009 Mario AI Competition,
which was run in association with the IEEE Games Innovation
Conference and the IEEE Symposium on Computational
Intelligence and Games. The focus of the competition was on
developing controllers that could play a version of Super Mario
Bros as well as possible. We describe the motivations for holding
this competition, the challenges associated with developing
artificial intelligence for platform games, the software and
API developed for the competition, the competition rules and
organization, the submitted controllers and the results. We
conclude the paper by discussing what the outcomes of the
competition can teach us both about developing platform game
AI and about organizing game AI competitions. The first two
authors are the organizers of the competition, while the third
author is the winner of the competition.
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===== Thesis =====

Robin Baumgarten\\
//"Combining Artificial Intelligence Methods:
Automating the Playing of DEFCON"// {{track>:thesis.pdf|PDF||87802}}\\
Master Thesis, Imperial College London, 2007.
\\
++++ Abstract |

In the commercial video game industry, computer opponents that act intelligently
are increasingly important, especially as better graphical effects decline to serve
as a driving force for the commercial success of a game. The methods used by
developers to create these bots are often obsolete and struggle to scale with the
complexity of modern games. Nonetheless the use of modern artificial intelligence
techniques used by researchers is rarely seen in video games.
In this project, we designed and implemented a computer opponent for the real-
time strategy game DEFCON by combining artificial intelligence methods such
as case-based reasoning, decision tree algorithms and hierarchical planning. High-level strategy plans for matches are automatically created by querying a case base
of recorded matches and building a plan decision tree.
The development of an automated opponent for a complex video game required
the application of many different techniques to receive, store, process and predict
game information. For this purpose, alongside a high-level reasoning system, we
use secondary AI techniques like simulated annealing and influence mapping to
create a reactive and learning bot. We applied these techniques in DEFCON and
created a competitive bot that can beat the AI bot developed by Introversion
consistently. The importance of small-scale tactics in this game requires careful unit
control, which we incorporated through various methods, such as a movement desire
model, fleet formations and a synchronous attack algorithm. Extensive testing was
conducted to optimise and fine-tune the efficiency of these optimisation algorithms.
Comprehensive training of high-level plans enabled our bot to learn potent strategies that provided a win ratio of over 75% against the official AI bot developed for
DEFCON by Introversion.
++++