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This work presents a research of the improvement of the Team AI in Unreal Tournament Bots by means of a Genetic Algorithm, which evolves the set of parameters that determines the behaviour of a bot inside a team. Presented at IEEE Computer Intelligence and Games (CIG 2010). IT University of Copenhagen, Denmark. -------------------------------------------------- Este trabajo presenta la investigación llevada a cabo para mejorar la IA de los Bots en Unreal Tournament cuando éstos están dentro de un equipo. Para ello se ha utilizado un Algoritmo Genético que evoluciona el conjunto de parámetros de los que depende el comportamiento de un Bot dentro de un equipo. Presentada en el IEEE CIG 2010 (ITU, Copenhague, Dinamarca).
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A.M. Mora, M.A. Moreno, J.J. Merelo, P.A. Castillo, M.G. Arenas, J.L.J. Laredo
Dpto. ATC
University of Granada
CIG2010
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Unreal? GAs? Teams? Results? Conclusions? That’s all?
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It is a First Person Shooter (FPS): Very famous since 1998, because it had an excellent enemies’ AI, so it was (and is) a very good game for multiplayer battlematches between users and the so-called Bots (authonomous players).
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It offers the Unreal Editor (included with the game): It is easy to create Mods:
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There is an own language (UnrealScript). The editor is also an IDE, since it is possible to access the whole tree of classes, and write and compile new code inside it. But it has some flaws such as a limited size for arrays, its is slower than other languages, or the dificulty to debug the code.
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It is based on a Finite State Machine:
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Exa
mpl
e: S
tate
Roa
min
g
A Bot decides what to do (changing between the states and substates) in the FSM depending on some parameters or thresholds. For instance: (in Roaming state)
IF distance_to_enemy() < MIN_DIST_SHOOT THEN
go_to_state(Attacking)
In addition, there are some values used to calculate others, such as penalties, multiplication factors, etc.
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Put them into
an array
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Identify the ‘main’
set of parameters
Evolve it with an Evolutionary Algorithm
(Genetic Algorithm)
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Created by Johann Dréo
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Genetic Algorithm
based bot (GA-Bot)
GA
EVOLUTIONARY
PROCESS
Std
AI
population
Std
AI
Std
AI
FITNESS EVALUATION
Put them into
an array
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Identify the ‘main’
set of parameters
(for a Team)
Evolve it with an Evolutionary Algorithm
(Genetic Algorithm)
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Genetic Algorithm
based team of bots
(GT-Bot)
GA
EVOLUTIONARY
PROCESS
population
FITNESS EVALUATION
Std
AI Std
AI
Std
AI
vs
OR
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cr-Team All the bots in the team have the same chromosome.
cr-Bot Every bot in the team has a different chromosome. A shape of co-evolution.
The set of thresholds and parameters to consider in a Team evolution is:
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The real-coded GA we have used in this work considers: Chromosome Lentgh 40 Population size 30
Number of Generations 30 Simple Gene Mutation
Mutation probability 0.01 Two point crossover Crossover probability 0.6 Time per chromosome 90 seconds
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And the Fitness Function: Each team is evaluated after 90 seconds The fitness is a sumatory considering the bots’ results: - number of frags - times defeated - number of items collected - weapons collected - power of weapons
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1 cr-Bot based Team (2 Bots) vs 3 standard 2-bots teams
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1 cr-Team based Team (2 Bots) vs 3 standard 2-bots teams
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1 cr-Team based Team (4 Bots) vs 3 standard 4-bots teams
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1 cr-Bot based Team (2 Bots) vs 1 cr-Team based Team (2 bots) vs 2 standard 2-bots teams
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vs
We have improved the AI of Unreal Bots using a Genetic Algorithm, so they can behave better in a team. We have implemented 2 approaches:
• one based in having a different set of parameters per bot (co-evolution) – cr-Bot • another based in sharing all the bots in the team the same set of parameters – cr-Team
The results show that both approaches performs better than the standard bots and the cr-Team is the best of them. FUTURE WORK: - perform more experiments - improve the FSM of the AI for teams (sets of rules)
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