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Automated Video Game Testing Using Synthetic and Human-Like Agents
[article]
2019
arXiv
pre-print
In this paper, we present a new methodology that employs tester agents to automate video game testing. We introduce two types of agents -synthetic and human-like- and two distinct approaches to create them. Our agents are derived from Reinforcement Learning (RL) and Monte Carlo Tree Search (MCTS) agents, but focus on finding defects. The synthetic agent uses test goals generated from game scenarios, and these goals are further modified to examine the effects of unintended game transitions. The
arXiv:1906.00317v1
fatcat:7n2lynwyivbqxaea34ejckfsxa