Proceedings of the Annual Meeting of the Cognitive Science Society

Remaining committed to a joint goal in the face of many entic- ing alternatives is challenging. Doing so while cooperating with others under uncertainty is even more so. Despite this, agents can successfully and robustly use bootstrapping to con- verge on a joint intention from randomness under the Imagined We framework. We demonstrate the power of this model in a real-time cooperative hunting task. Additionally, we run a suite of model experiments to answer some of the potential chal- lenges to converging that this model could face under imperfect conditions. Specifically, we ask what happens when (1) there are increasingly many equivalent choices? (2) I only have an approximate model of you? and (3) my perception is noisy? We show through a set of model experiments that this framework is robust to all three of these manipulations.