Human-in-the-Loop Synthesis for Partially Observable Markov Decision Processes

TL;DR

This paper introduces a human-in-the-loop approach for synthesizing strategies in POMDPs by collecting human input through simulation, translating it into a Markov chain, and verifying its safety, significantly improving scalability.

Contribution

It presents a novel method combining human demonstrations with behavior cloning to synthesize and verify strategies for POMDPs efficiently.

Findings

Scalability improved by orders of magnitude.

Behavior cloning effectively translates human input into POMDP strategies.

Counterexample-based refinement enhances strategy quality.

Abstract

We study planning problems where autonomous agents operate inside environments that are subject to uncertainties and not fully observable. Partially observable Markov decision processes (POMDPs) are a natural formal model to capture such problems. Because of the potentially huge or even infinite belief space in POMDPs, synthesis with safety guarantees is, in general, computationally intractable. We propose an approach that aims to circumvent this difficulty: in scenarios that can be partially or fully simulated in a virtual environment, we actively integrate a human user to control an agent. While the user repeatedly tries to safely guide the agent in the simulation, we collect data from the human input. Via behavior cloning, we translate the data into a strategy for the POMDP. The strategy resolves all nondeterminism and non-observability of the POMDP, resulting in a discrete-time Markov chain (MC). The efficient verification of this MC gives quantitative insights into the quality of the inferred human strategy by proving or disproving given system specifications. For the case that the quality of the strategy is not sufficient, we propose a refinement method using counterexamples presented to the human. Experiments show that by including humans into the POMDP verification loop we improve the state of the art by orders of magnitude in terms of scalability.