Real-world scenarios often involve dynamic interactions among competing agents, where decisions are made considering actions taken by others. These situations can be modeled as partially observable stochastic games (POsts), POst s), with zero-sum variants capturing strictly competitive interactions (e.g., security scenarios). While such models address a broad range of problems, they commonly focus on infinite-horizon scenarios with discounted-sum objectives. Using the discounted-sum objective, however, can lead to suboptimal solutions in cases where the length of the interaction does not directly affect the gained rewards of the players. We thus focus on games with undiscounted objective and an indefinite horizon where every realization of the game is guaranteed to terminate after some unspecified number of turns. To manage the computational complexity of solving POsts s in general, we restrict to games with one-sided partial observability where only one player has imperfect information while their opponent is provided with full information about the current situation. We introduce two novel algorithms based on the heuristic search value iteration ( HsVI ) algorithm that iteratively solve sequences of easier-to-solve approximations of the game using fundamentally different approaches for constructing the sequences: (1) in toalHorizon, , the game approximations are based on a limited number of turns in which players can change their actions, (2) in toalDiscount, , the game approximations are constructed using an increasing discount factor. We provide theoretical qualitative guarantees for algorithms, and we also experimentally demonstrate that these algorithms are able to find near-optimal solutions on pursuit-evasion games and a game modeling privilege escalation problem from computer security.
