Risk Management: Anticipating and Reacting in StarCraft

Abstract

Managing risk with imperfect information is something humans do every day, but we have little insight into the abilities of AI agents to do so. We define two risk management strategies and perform an ability-based evaluation using StarCraft agents. Our evaluation shows that nearly all agents mitigate risks after observing them (react), and many prepare for such risks before their appearance (anticipate). For this evaluation, we apply traditional causal effect inference and causal random forest methods to explain agent behavior. The results highlight different risk management strategies among agents, others strategies that are common to agents, and overall encourage evaluating agent risk management abilities in other AI domains.