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An overview on the termination conditions in the
evolution of game bots
A. Fernández-Ares, P. García-Sánchez, A.M. Mora, P.A. Castillo, J.J. Merelo, M.G. Arenas
Universidad de Granada (Spain)
A summary of previous worksPablo García-Sánchez, Antonio Fernández-Ares, Antonio Miguel Mora, Pedro A. Castillo Valdivieso, Jesús González, Juan Julián Merelo Guervós: Tree Depth Influence in Genetic Programming for Generation of Competitive Agents for RTS Games. EvoApplications 2014: 411-421
Antonio Fernández-Ares, Pablo García-Sánchez, Antonio Miguel Mora, Pedro A. Castillo, Juan Julián Merelo Guervós: Designing competitive bots for a real time strategy game using genetic programming. CoSECivi 2014: 159-172
Antonio Fernández-Ares, Pablo García-Sánchez, Antonio M. Mora, Pedro A. Castillo Valdivieso, Juan Julián Merelo Guervós, Maria I. García Arenas, Gustavo Romero: It's Time to Stop: A Comparison of Termination Conditions in the Evolution of Game Bots. EvoApplications 2015: 355-368
Index
1. Introductiona. RTSb. Planet Warsc. Genetic
Programming2. GPBot
a. Conditionsb. Actions
3. SCORE - Fitness4. Methodology
a. Termination Criteria5. Experimental Setup
6. Resultsa. Scoreb. Generationsc. Testsd. Benchmarke. Comparative
7. Conclusions8. Future Work
Introduction: RTS
Real-Time Strategy games (RTS-games)● Resources
● Units
● Buildings
Victory: Get all resources, kill all enemy units or destroy enemy buildings
Introduction: Planet Wars
Buildings:Planets
Units:Space ships
Resources:Ships generated in every planet
Introduction: Genetic Programming
Evolutionary Algorithm that evolves binary decision tree➔ Internal nodes: Conditions➔ Leafs: Actions
Individual -> behavioural model (solution)Evaluation -> Playing a game against a rival and getting a scoreAdapted operators
GPBot: Conditions
A logical expression composed by, at least, one extracted standard game state variable and a value between 0 and 1
● myShipsEnemyRatio● myShipsLandedFlyingRatio● myPlanetsEnemyRatio● myPlanetsTotalRatio● actualMyShipsRatio● actualLandedFlyingRatio
GPBot: Actions
The possible actions just involve the movement of an amount of ships from a source to a destination planet
● Attack Nearest (Neutral|Enemy|NotMy)Planet● Attack Weakest (Neutral|Enemy|NotMy) Planet● Attack Wealthiest (Neutral|Enemy|NotMy)
Planet● Attack Beneficial (Neutral|Enemy|NotMy)
Planet● Attack Quickest (Neutral|Enemy|NotMy) Planet}● Attack (Neutral|Enemy|NotMy) Base● Attack Random Planet● Reinforce Nearest Planet● Reinforce Base● Reinforce Wealthiest Planet
Score (Fitness)
SCORE BASED IN TURNS IN
VICTORIES
SCORE BASED IN TURNS IN DEFEATS
SCORE BASED IN
VICTORIES> >
Methodology: Termination Criteria
[NG] Number of Generations ➔ 30, 50, 100, 200 Generations
[AO] Age of Outliers ➔ 1, 1.5, 2, 2.5 times the IQR
[RT] Replacement Rate➔ n/2, n/4, n/8, n/16 with n = N/2
[FT] Fitness Threshold➔ 22, 24, 26, 28
[FI] Fitness Improvement➔ 3, 7, 10, 15
Experimental SetupParameter Name Value
Population Size (N) 32
Number of battles for scoring (NB) 30
Re-evaluation of individuals Yes
Crossover type Sub-tree crossover
Crossover rate 0.5
Mutation 1-node mutation
Mutation rate 0.25
Selection 2-tournament
Replacement Generational
Maximum Tree Depth 7
Runs 36
SCORE of the best individuals (of all runs) grouped by criterion
GENERATION attained (of all runs) grouped by criterion
Kruskal-Wallis Test of samples of each criterion by SCORE an GENERATIONS.
Black means no statistically significant difference has been found.
Percentage of victories (of the 36 best individuals) in benchmark against five different competitive bots available in the literature in 100 maps
Linear regression of the SCORE (fitness) with the results of the benchmark (Percentage of victories)
Average score of the best individual and average reached generations per
termination criteria
Average results of every criterion(relative with respect to NG_30)
Conclusions I
[AO] � Not good. As more restrictive values don’t imply a significant score improvement.[FT]� Best score. But the optimum fitness might not be know. Needs the highest amount of generations. It might not finish.[FI]� Useful to ‘detect’ local optima.[RT]� Provides the best results considering all metrics.
Conclusions II
Fitness Threshold would be the most desirable option➔ It attains the best score.➔ It is quite difficult to find an optimal fitness value to
use (normally it is unknown).➔ Requires more computational budget, and it is
possible that it never ends (the criterion is not met).
Replacement Rate as stopping criterion, since it is a compromise solution which relies in the population improvement without an implicit use of the fitness.
Future work
● New problems (and algorithms) are being addressed.○ P. García-Sánchez, Alberto Tonda, Giovanni
Squillero and JJ. Merelo: Towards Automatic StarCraft Strategy Generation Using Genetic Programming. Accepted at CIG 2015.
● Mechanisms to improve the EA.● Use larger and complex decision trees.
Questions
Thank you!Antonio Ferná[email protected] - @antaress
Antonio M. [email protected] - @amoragar
Pablo García-Sá[email protected] - @fergunet