Automata Learning -- Expect Delays!

TL;DR

This paper introduces an efficient active automata learning method that separately learns behavior and stochastic delays, improving accuracy and reducing oversampling in models with delay variability.

Contribution

It proposes a novel approach to learning automata with stochastic delays by separating behavior and delay estimation, enhancing efficiency and accuracy.

Findings

Outperforms naive delay sampling methods in benchmarks

Effectively distinguishes behaviors with similar outputs but different delays

Demonstrates applicability in realistic database join order scenarios

Abstract

This paper studies active automata learning (AAL) in the presence of stochastic delays. We consider Mealy machines that have stochastic delays associated with each transition and explore how the learner can efficiently arrive at faithful estimates of those machines, the precision of which crucially relies on repetitive sampling of transition delays. While it is possible to na\"ively integrate the delay sampling into AAL algorithms such as $L^*$, this leads to considerable oversampling near the root of the state space. We address this problem by separating conceptually the learning of behavior and delays such that the learner uses the information gained while learning the logical behavior to arrive at efficient input sequences for collecting the needed delay samples. We put emphasis on treating cases in which identical input/output behaviors might stem from distinct delay characteristics. Finally, we provide empirical evidence that our method outperforms the na\"ive baseline across a wide range of benchmarks and investigate its applicability in a realistic setting by studying the join order in a relational database.