Estimating cognitive biases with attention-aware inverse planning
This work addresses the need for autonomous systems to understand human cognitive biases during interactions, such as in driving, but it is incremental as it builds on existing computational cognitive science methods.
The paper tackles the problem of estimating people's cognitive biases from their actions by introducing attention-aware inverse planning, which differs from standard inverse reinforcement learning. It demonstrates an approach combining deep reinforcement learning with cognitive modeling to infer attentional strategies in real-life driving scenarios using the Waymo Open Dataset, showing scalability.
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.