Many multi-robot decision problems present autonomous agents with a dual challenge: the accurate egocentric estimation of the state and strategy of their adversaries, in the face of physical limitations and sensory uncertainty. Although these are clearly difficult constraints on the capabilities of an autonomous robot, this is also an opportunity for exploiting the corresponding limitations of the adversary. In this paper, we propose a decision making framework for physically constrained multi-robot games, using a combination of probabilistic and game-theoretic tools. We first present the Reachable Set Particle Filter, an adversary state estimation algorithm combining data-driven approximation with dynamical constraints. Then, we use game-theoretic notions to formulate a strategy estimation framework that progressively learns and exploits the adversary's behaviour. We evaluate our framework in a series of robotic soccer games between robots with varying sensing and strategic capabilities. Our results demonstrate that the combination of probabilistic modeling and strategic reasoning leads to significant improvements in performance robustness, while flexibly adapting to dynamic adversaries.
|Title of host publication||Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||7|
|Publication status||Published - 2011|