Maximizing Legibility in Stochastic Environments
Shuwa Miura, Andrew L. Cohen, Shlomo Zilberstein · 2021
Making an agent’s intentions clear from its observed behavior is crucial for seamless human-agent interaction and for increased transparency and trust in AI systems. Existing methods that address this challenge and maximize legibility of behaviors are limited to deterministic domains. We develop a technique for maximizing legibility in stochastic environments and illustrate that using legibility as an objective improves interpretability of agent behavior in several scenarios. We provide initial empirical evidence that human subjects can better interpret legible behavior.