# Tree-Based Adaptive Model Learning

arXiv: https://arxiv.org/abs/2209.00122 · HTML: https://mlsanity.com/paper/2209.00122/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 16 references that are in the map.
- First posted on arXiv 31 Aug 2022.

## Builds on (most cited first, 1 of 1)

- [Prognosis: Closed-Box Analysis of Network Protocol Implementations](https://mlsanity.com/paper/2201.02577/) (2021)

## Built on by (most cited first, 4 of 4)

- [State Matching and Multiple References in Adaptive Active Automata Learning](https://mlsanity.com/paper/2406.19714/) (2024)
- [Conflict-Aware Active Automata Learning (Extended Version)](https://mlsanity.com/paper/2308.14781/) (2023)
- [Conflict-Aware Active Automata Learning](https://mlsanity.com/paper/2310.01003/) (2023)
- [Error-awareness Accelerates Active Automata Learning](https://mlsanity.com/paper/2602.21674/) (2026)

## Abstract (arXiv)

We extend the Kearns-Vazirani learning algorithm to be able to handle systems that change over time. We present a new learning algorithm that can reuse and update previously learned behavior, implement it in the LearnLib library, and evaluate it on large examples, to which we make small adjustments between two runs of the algorithm. In these experiments our algorithm significantly outperforms both the classic Kearns-Vazirani learning algorithm and the current state-of-the-art adaptive algorithm.