Learning Markov Decision Processes for Model Checking

TL;DR

This paper presents a new algorithm for automatically learning deterministic labeled Markov decision process models from reactive system behaviors, enhancing model checking efficiency.

Contribution

It extends existing algorithms to handle reactive systems with both probabilistic and nondeterministic transitions, and adapts them for learning MDPs from observed behaviors.

Findings

Successfully learned models of slot machines

Analyzed probabilistic linear temporal logic properties

Evaluated optimal schedulers from learned models

Abstract

Constructing an accurate system model for formal model verification can be both resource demanding and time-consuming. To alleviate this shortcoming, algorithms have been proposed for automatically learning system models based on observed system behaviors. In this paper we extend the algorithm on learning probabilistic automata to reactive systems, where the observed system behavior is in the form of alternating sequences of inputs and outputs. We propose an algorithm for automatically learning a deterministic labeled Markov decision process model from the observed behavior of a reactive system. The proposed learning algorithm is adapted from algorithms for learning deterministic probabilistic finite automata, and extended to include both probabilistic and nondeterministic transitions. The algorithm is empirically analyzed and evaluated by learning system models of slot machines. The evaluation is performed by analyzing the probabilistic linear temporal logic properties of the system as well as by analyzing the schedulers, in particular the optimal schedulers, induced by the learned models.