Beyond Winning Strategies: Admissible and Admissible Winning Strategies for Quantitative Reachability Games
Abstract
Classical reactive synthesis approaches aim to synthesize a reactive system that always satisfies a given specification. These approaches often reduce to playing a two-player zero-sum game where the goal is to synthesize a winning strategy. However, in many pragmatic domains, such as robotics, a winning strategy does not always exist, yet it is desirable for the system to make an effort to satisfy its requirements instead of "giving up." To this end, this paper investigates the notion of admissible strategies, which formalize "doing-your-best", in quantitative reachability games. We show that, unlike the qualitative case, memoryless strategies are not sufficient to capture all admissible strategies, making synthesis a challenging task. In addition, we prove that admissible strategies always exist but may produce undesirable optimistic behaviors. To mitigate this, we propose admissible winning strategies, which enforce the best possible outcome while being admissible. We show that both strategies always exist but are not memoryless. We provide necessary and sufficient conditions for the existence of both strategies and propose synthesis algorithms. Finally, we illustrate the strategies on gridworld and robot manipulator domains.
Cite
Text
Muvvala et al. "Beyond Winning Strategies: Admissible and Admissible Winning Strategies for Quantitative Reachability Games." International Joint Conference on Artificial Intelligence, 2025. doi:10.24963/IJCAI.2025/25Markdown
[Muvvala et al. "Beyond Winning Strategies: Admissible and Admissible Winning Strategies for Quantitative Reachability Games." International Joint Conference on Artificial Intelligence, 2025.](https://mlanthology.org/ijcai/2025/muvvala2025ijcai-beyond/) doi:10.24963/IJCAI.2025/25BibTeX
@inproceedings{muvvala2025ijcai-beyond,
title = {{Beyond Winning Strategies: Admissible and Admissible Winning Strategies for Quantitative Reachability Games}},
author = {Muvvala, Karan and Ho, Qi Heng and Lahijanian, Morteza},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2025},
pages = {214-222},
doi = {10.24963/IJCAI.2025/25},
url = {https://mlanthology.org/ijcai/2025/muvvala2025ijcai-beyond/}
}