Simulation of autonomous vehicle systems requires that simulated traffic participants exhibit diverse and realistic behaviors.

In IMP, a multi-component engineering system is subject to a risk of failure due to its components' damage condition.

This paper explores the potential of building scalable techniques to facilitate autonomous cooperation among communicative agents, and provides insight into their "cognitive" processes. To address the challenges of achieving autonomous cooperation, we propose a novel communicative agent framework named roleplaying .

Domain transfer methods aim to bridge this dynamics gap through techniques such as domain adaptation or domain calibration.

In many machine learning tasks, there are commonly sources of exogenous and endogenous distribution shift, necessitating that the algorithm be retrained repeatedly over time.

In this paper, we show that partner specialization, in addition to diversity, is crucial for the robustness of a downstream generalist agent.