POMDP to the Rescue: Boosting Performance for Robocup Rescue

Disaster response is one of the most critical social issues and introduces quite a few research themes for the AI planning area. Robocup Rescue provides a platform to simulate the rescue process in a city when an earthquake happens. Existing methods consist of multi-agent methods that use greedy heuristics. These methods scale to large maps but suffer from volatile performance under different scenarios. In this work, we propose a planning framework to boost the performance on Robocup Rescue given several policies from the competition to be used as components. More specifically, we use an online POMDP algorithm with macro-actions and restrict it to plan within the space of tasks performed by the agents in the component policies at each time instance. Since the action space contains macro-actions of the component policies, the method is guaranteed to perform at least as well as the best component policy, and possibly better, if sufficient computation is provided. On the other hand, the restriction of the tasks to those suggested by component policies reduces the computational complexity of planning and allows the planning method to be practically applied. Experiment results show that our planner generates better performance than the best component policy for some scenarios and gives performance comparable to the best component policy for the rest.