Online diverse content generation via multi-objective ensemble pruning
TONG Hao
ZHANG Qing-quan
YUAN Bo
WANG Han-ding
LIU Jia-lin
Abstract：Online diverse content generation is one of the emergent research directions in the field of procedural content generation in recent years.It can not only meet users' different preferences and enhance user experience,but also provide a large amount of scenarios and problems for training and testing artificial intelligence algorithms.Recent research proposed online diverse content generation methods based on negatively correlated ensemble reinforcement learning,such methods can not effectively meet the specific preferences of different users.Furthermore,training and deploying individual learning models requires significant computational resources.To address those two issues,this paper proposes an online content generation approach based on multi-objective ensemble pruning,built upon the negatively correlated ensemble reinforce-ment learning framework.This approach searches for the weights for integrating individual learning models through an efficient multi-objective optimization algorithm,so that the obtained ensemble model can not only effectively match user preferences,but also offer a Pareto set that exhibits a tradeoff between model performance and computational resource consumption.This approach matches user preferences by adjusting the weights of individual learning models instead of retraining models,thereby reducing the computational resource consumption.
Keywords：procedural content generationonline content generationmulti-objective optimizationevolutionary algo-rithmsensemble learningvideo games
Publication Date：2025-11-30
Online Publishing Date：2025-12-29(First online date of this platform, not the publication date of the document)
Pages：11( 2352-2362 )