Abstrakt
Many contemporary computer games, notably action and role-playing games, represent an interesting class of navigation-intensive dynamic real-time simulations inhabited by autonomous intelligent virtual agents (IVAs). Although higher level reasoning of IVAs in these domains seems suited for action planning, planning is not widely adopted in existing games and similar applications.
Moreover, statistically rigorous study measuring performance of planners in decision making in a game-like domain is missing. Here, five classical planners were connected to the virtual environment of Unreal Development Kit along with a planner for delete-free domains (only positive preconditions and positive effects).
Performance of IVAs employing those planners and IVAs with reactive architecture was measured on a class of game-inspired test environments of various sizes and under different levels of external interference. The analysis has shown that planning agents outperform reactive agents if (i) the size of the problem is small or if (b) the environment changes are either hostile to the agent or infrequent.
In delete-free domains, specialized approaches are inferior to classical planners because the lower expressivity of delete-free domains results in lower plan quality. These results can help to determine when planning is advantageous in games and for IVAs control in other dynamic real-time environments.