  The Angry Birds AI competition has been held over many years to encourage the
development of AI agents that can play Angry Birds game levels better than
human players. Many different agents with various approaches have been employed
over the competition's lifetime to solve this task. Even though the performance
of these agents has increased significantly over the past few years, they still
show major drawbacks in playing deceptive levels. This is because most of the
current agents try to identify the best next shot rather than planning an
effective sequence of shots. In order to encourage advancements in such agents,
we present an automated methodology to generate deceptive game levels for Angry
Birds. Even though there are many existing content generators for Angry Birds,
they do not focus on generating deceptive levels. In this paper, we propose a
procedure to generate deceptive levels for six deception categories that can
fool the state-of-the-art Angry Birds playing AI agents. Our results show that
generated deceptive levels exhibit similar characteristics of human-created
deceptive levels. Additionally, we define metrics to measure the stability,
solvability, and degree of deception of the generated levels.
