Action Window Planning for Stealth Missions

Kaijie Xu, Clark Verbrugge

Abstract: Action windows—spatiotemporal regions enabling player's safe execution of key in-game actions—are foundational to game task planning, yet their automated generation remains underexplored. In stealth games, for example, level designers carefully create guard patrols and environment layouts. However, critical tasks such as planning assassination routes for high-value targets (VIPs) still depend heavily on manual tuning. This work formalizes VIP task planning as the problem of automatically generating a path through a predefined environment with guard patrols, such that VIP's path contains player's safe action windows that are temporally and spatially dispersed, while maintaining coherence and meaningful interactions with environmental elements. We introduce two approaches: (1) an evolutionary optimization approach that is efficient in generating diverse routes by balancing multiple objectives, and (2) a constraint-driven safe-block search method that guarantees optimal sequences under strict design thresholds. Initial experiments validate that the evolutionary method generates diverse, high-dispersion routes with rapid runtimes, whereas the safe-block approach enforces hard constraints with predictable performance. Both methods integrate directly with existing level and patrol data, offering scalable solutions for automated stealth mission generation.