Automatic Discovery of Interpretable Planning Strategies
This addresses the challenge of designing effective decision aids for professionals like medical doctors by making automated strategy discovery interpretable, representing an incremental advance in leveraging AI to improve human decision-making.
The paper tackled the problem of opaque policies from reinforcement learning by introducing AI-Interpret, a method to transform them into interpretable decision rules like flowcharts, resulting in significant improvements in human planning strategies and decisions across three sequential decision problems, with experiments showing it outperforms performance feedback training.
When making decisions, people often overlook critical information or are overly swayed by irrelevant information. A common approach to mitigate these biases is to provide decision-makers, especially professionals such as medical doctors, with decision aids, such as decision trees and flowcharts. Designing effective decision aids is a difficult problem. We propose that recently developed reinforcement learning methods for discovering clever heuristics for good decision-making can be partially leveraged to assist human experts in this design process. One of the biggest remaining obstacles to leveraging the aforementioned methods is that the policies they learn are opaque to people. To solve this problem, we introduce AI-Interpret: a general method for transforming idiosyncratic policies into simple and interpretable descriptions. Our algorithm combines recent advances in imitation learning and program induction with a new clustering method for identifying a large subset of demonstrations that can be accurately described by a simple, high-performing decision rule. We evaluate our new algorithm and employ it to translate information-acquisition policies discovered through metalevel reinforcement learning. The results of large behavioral experiments showed that prividing the decision rules generated by AI-Interpret as flowcharts significantly improved people's planning strategies and decisions across three diferent classes of sequential decision problems. Moreover, another experiment revealed that this approach is significantly more effective than training people by giving them performance feedback. Finally, a series of ablation studies confirmed that AI-Interpret is critical to the discovery of interpretable decision rules. We conclude that the methods and findings presented herein are an important step towards leveraging automatic strategy discovery to improve human decision-making.