An approach for automatically determining the possible actions in computer game states

Due to the great difficulty of thoroughly testing video game software by hand, it is desirable to have AI agents that can automatically explore different game functionalities. A key requirement of such agents is a model of the player actions that the agent can use to both determine the set of possible actions in different game states, as well as perform a chosen action on the game selected by the agent's policy. The typical game engines that are in use today do not offer such a model of actions, leading existing work to either require human effort to manually define the action model or imprecisely guess the possible actions. In our work, we demonstrate how program analysis is an effective solution to this problem by developing a state-of-the-art analysis for the user input handling logic present in games that can automatically model game actions with a discrete action space. Our key insight is that the possible actions of games correspond to the different execution paths that can be taken through the user input handling logic present in the game's code.