通过行为反推人的注意力偏差，让系统更懂人类决策习惯。
Estimating cognitive biases with attention-aware inverse planning
- 结合深度强化学习与认知建模，从行为逆向推断注意力偏差。
- 在Waymo数据集上验证，能有效识别真实驾驶场景中的注意力策略。
- 为交互式系统提供理解人类认知偏差的新方法，适合人机协作研究者。
人类的目标导向行为受认知偏差影响，与人类互动的自主系统需具备对此的认知能力。例如，人在环境中对物体的注意力存在系统性偏差，从而影响日常任务（如通勤驾驶）表现。本文基于计算认知科学最新成果，正式提出注意力感知逆向规划问题：从个体行为中估计其注意力偏差。我们揭示了该方法与传统逆强化学习的本质差异，并展示了如何从行为中推断认知偏差。进一步提出一种融合深度强化学习与计算认知建模的方法，应用于从Waymo Open Dataset中选取的真实驾驶场景，成功推断出强化学习代理的注意力策略，验证了该方法在大规模场景下的可扩展性。
原文摘要 · Abstract (English)
People's goal-directed behaviors are influenced by their cognitive biases, and autonomous systems that interact with people should be aware of this. For example, people's attention to objects in their environment will be biased in a way that systematically affects how they perform everyday tasks such as driving to work. Here, building on recent work in computational cognitive science, we formally articulate the attention-aware inverse planning problem, in which the goal is to estimate a person's attentional biases from their actions. We demonstrate how attention-aware inverse planning systematically differs from standard inverse reinforcement learning and how cognitive biases can be inferred from behavior. Finally, we present an approach to attention-aware inverse planning that combines deep reinforcement learning with computational cognitive modeling. We use this approach to infer the attentional strategies of RL agents in real-life driving scenarios selected from the Waymo Open Dataset, demonstrating the scalability of estimating cognitive biases with attention-aware inverse planning.
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