  The complexity of computer games is ever increasing. In this setup, guiding
an automated test algorithm to find a solution to solve a testing task in a
game's huge interaction space is very challenging. Having a model of a system
to automatically generate test cases would have a strong impact on the
effectiveness and efficiency of the algorithm. However, manually constructing a
model turns out to be expensive and time-consuming. In this study, we propose
an online agent-based search approach to solve common testing tasks when
testing computer games that also constructs a model of the system on-the-fly
based on the given task, which is then exploited to solve the task. To
demonstrate the efficiency of our approach, a case study is conducted using a
game called Lab Recruits.
