Evolving Bot AI in Unreal (Poster EVOGames 2010, in EVO* 2010)

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Poster describing the evolutionary process for improving the Bots AI in the PC game Unreal. Presented at EVO* 2010 (in EVOGames Workshop)

Technologie

Preliminary Results
Depto. Arquitectura y Tecnología de Computadores
Universidad de Granada (Spain)
Contact: ANTONIO M. MORA (amorag@geneura.ugr.es)
What’s/Why Unreal?
Is a First Person Shooter (FPS) by Epic Games.
From Unreal in 1998 to Unreal Tournament 3 in 2007.
Very good AI for the standard bots (autonomous players).
Open programming environment (UnrealEd) and native
language (UnrealScript).
Instituto Tecnológico de Informática
Universidad Politécnica de Valencia (Spain)
Unreal (1998) Unreal Tournament 3 (2007)
ROAMING State (Substates and flow lines)
UnrealEd (UnrealScript)
AI in Unreal
Each bot follows a finite state machine (FSM) with plenty
of states and substates.
The transition in the FSM depends on some parameters
(usually hard-coded) which models the status, location in the
map (scenario) or relationship with the enemies.
The whole FSM (or just a part/state) can be modeled as a
tree of rules.
Genetic Bots (G-Bots)
Genetic Algorithm
based bot (GA-Bot),
which evolves a set of
parameters.
Genetic Programming
based bot (GP-Bot),
which evolves the set of
rules which defines the
state transitions.
DEPENDS
ON
MODELS
GA
EVOLUTIONARY
PROCESS
PG
EVOLUTIONARY
PROCESS
Standar
d AI
Standar
d AI
Standar
d AI
population
population
Evaluation(Fitnesscalculation)
Evaluation Function
Each individual in the population is an AI ‘representation’ (a set
of parameters or rules), which models a behaviour.
The fitness calculation is performed by assigning the individual
as the G-Bot’s AI, and placing it into a scenario to fight against
some standard (AI) bots.
The fitness will be a combination of the number of killed
enemies, times the bot has been defeated, collected items and
weapons, and life time.

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1. Preliminary Results Depto. Arquitectura y Tecnología de Computadores Universidad de Granada (Spain) Contact: ANTONIO M. MORA (amorag@geneura.ugr.es) What’s/Why Unreal? Is a First Person Shooter (FPS) by Epic Games. From Unreal in 1998 to Unreal Tournament 3 in 2007. Very good AI for the standard bots (autonomous players). Open programming environment (UnrealEd) and native language (UnrealScript). Instituto Tecnológico de Informática Universidad Politécnica de Valencia (Spain) Unreal (1998) Unreal Tournament 3 (2007) ROAMING State (Substates and flow lines) UnrealEd (UnrealScript) AI in Unreal Each bot follows a finite state machine (FSM) with plenty of states and substates. The transition in the FSM depends on some parameters (usually hard-coded) which models the status, location in the map (scenario) or relationship with the enemies. The whole FSM (or just a part/state) can be modeled as a tree of rules. Genetic Bots (G-Bots) Genetic Algorithm based bot (GA-Bot), which evolves a set of parameters. Genetic Programming based bot (GP-Bot), which evolves the set of rules which defines the state transitions. DEPENDS ON MODELS GA EVOLUTIONARY PROCESS PG EVOLUTIONARY PROCESS Standar d AI Standar d AI Standar d AI population population Evaluation(Fitnesscalculation) Evaluation Function Each individual in the population is an AI ‘representation’ (a set of parameters or rules), which models a behaviour. The fitness calculation is performed by assigning the individual as the G-Bot’s AI, and placing it into a scenario to fight against some standard (AI) bots. The fitness will be a combination of the number of killed enemies, times the bot has been defeated, collected items and weapons, and life time.

Evolving Bot AI in Unreal (Poster EVOGames 2010, in EVO* 2010)

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