Automated Video Game Testing Using Synthetic and Humanlike Agents

نویسندگان

چکیده

In this article, we present a new methodology that employs tester agents to automate video game testing. We introduce two types of - synthetic and humanlike distinct approaches create them. Our are derived from Sarsa Monte Carlo tree search (MCTS) but focus on finding defects, while traditional game-playing maximizing scores. The agent uses test goals generated scenarios, these further modified examine the effects unintended transitions. extracted by our proposed multiple greedy-policy inverse reinforcement learning (MGP-IRL) algorithm trajectories. MGP-IRL captures policies executed human testers. use produce sequences, run with sequences. At each run, an automated oracle check for bugs. analyze method in parts-we compare success bug finding, evaluate similarity between collected 427 trajectories testers using General Video Game Artificial Intelligence (GVG-AI) framework created three games 12 levels contain 45 experiments reveal compete testers' performances. Moreover, show increases humanlikeness improving performance.

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ژورنال

عنوان ژورنال: IEEE transactions on games

سال: 2021

ISSN: ['2475-1502', '2475-1510']

DOI: https://doi.org/10.1109/tg.2019.2947597