Enhancing the Monte Carlo Tree Search Algorithm for Video Game Testing
  In this paper, we study the effects of several Monte Carlo Tree Search (MCTS)
modifications for video game testing. Although MCTS modifications are highly
studied in game playing, their impacts on finding bugs are blank. We focused on
bug finding in our previous study where we introduced synthetic and human-like
test goals and we used these test goals in Sarsa and MCTS agents to find bugs.
In this study, we extend the MCTS agent with several modifications for game
testing purposes. Furthermore, we present a novel tree reuse strategy. We
experiment with these modifications by testing them on three testbed games,
four levels each, that contain 45 bugs in total. We use the General Video Game
Artificial Intelligence (GVG-AI) framework to create the testbed games and
collect 427 human tester trajectories using the GVG-AI framework. We analyze
the proposed modifications in three parts: we evaluate their effects on bug
finding performances of agents, we measure their success under two different
computational budgets, and we assess their effects on human-likeness of the
human-like agent. Our results show that MCTS modifications improve the bug
finding performance of the agents.
