Automated Video Game Testing Using Synthetic and Human-Like Agents
  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 human-like agent uses test goals extracted by
our proposed multiple greedy-policy inverse reinforcement learning (MGP-IRL)
algorithm from tester trajectories. MGPIRL captures multiple policies executed
by human testers. These testers' aims are finding defects while interacting
with the game to break it, which is considerably different from game playing.
We present interaction states to model such interactions. We use our agents to
produce test sequences, run the game with these sequences, and check the game
for each run with an automated test oracle. We analyze the proposed method in
two parts: we compare the success of human-like and synthetic agents in bug
finding, and we evaluate the similarity between humanlike agents and human
testers. We collected 427 trajectories from human testers using the General
Video Game Artificial Intelligence (GVG-AI) framework and created three games
with 12 levels that contain 45 bugs. Our experiments reveal that human-like and
synthetic agents compete with human testers' bug finding performances.
Moreover, we show that MGP-IRL increases the human-likeness of agents while
improving the bug finding performance.
