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There can be only
one:Evolving RTS
Bots viaJoust
SelectionA. Fernández-Ares, P. García-Sánchez, A.M.
Mora, P.A. Castillo, J.J. Merelo
Universidad de Granada (Spain)
Index1.Introduction
a.RTSb.Planet Warsc.Genetic
Programmingd.Fitness
Calculation Problem
2.SurvivalBota.Joust-based
selectionb.Replacement of
losers
3.Experimentsa.Experimental
Setupb.SurvivalBot vs
GPBotc.Evolution of
Average Scored.Number of
Victoriese.Evolution of Agef.Analysis of the
Score Uncertainty g.SurvivalBot vs
World4.Conclusions
5. Future Work
Introduction: RTSReal-Time Strategy games (RTS-games)
Resources
Units
BuildingsVictory: Get all resources, kill all enemy’s units or destroy enemy’s buildings
Introduction: Planet WarsBuildings:Planets
Units:Space ships
Resources:Ships generated in every planet
Introduction: Genetic ProgrammingEvolutionary Algorithm that evolves binary decision trees➔Internal nodes: Conditions➔Leaves: Actions
Individual -> behavioural model (solution)Evaluation -> Playing a game against a rival and getting a scoreAdapted operators ->
Fitness calculation problem
Joust-based Selection
Replacement of losers
Experiments and ResultsParameter Name Value
Population size 32
Initialization Random (trees of 3 levels)
Crossover type Sub-tree crossover
Crossover rate 0.5
Maximum number of turns per battle 1000
Mutation 1-node mutation
Mutation step-size 0.25
Selection 2-tournament
Replacement Steady-state
Stop criterion 4000 iterations
Maximum Tree Depth 7
Runs per configuration 30
Maps used in each evaluation 1 random chosen among maps {76,69,7,11,26}
SurvivalBot vs GPBot
Evolution of Average Score
Number of Victories
Evolution of Age (generations)
Analysis of the Score Uncertainty
Noise measurement:
maximun - minimum score:
30 best bots
10 maps (5 trained)
30 battles per map
SurvivalBot Vs World
BotName Simulations in training
BullyBot None*
SurvivalBot 8 000
Genebot 32 000
ExpGenebot 32 000
GPBot 8 000
HotFBot 180 000
Conclusions I
General-fighting bots:Non-specialized against specific
opponentDo not need a rival for evaluation
Conclusions II
Reduce number of battles:Reduce computational time
Conclusions III
Less affected by noise:Loss a match ➡ Remove from poolHigh selective pressure
Future workNew problems (and algorithms) will be addressed.
Mechanisms to improve the EA.Use larger and complex decision trees.
Questions?
Thank you!Antonio Ferná[email protected] - @antaress
Pablo García-Sá[email protected] - @fergunet
Antonio M. [email protected] - @amoragar
