Active Learning for Sound Negotiations

TL;DR

This paper introduces two active learning algorithms tailored for sound deterministic negotiations, a class of models for distributed systems, demonstrating their efficiency and ability to minimize such negotiations.

Contribution

The paper presents novel active learning algorithms for sound deterministic negotiations, leveraging their structure to achieve polynomial query complexity similar to Angluin's L* algorithm.

Findings

Algorithms have polynomial query complexity

Both algorithms effectively minimize negotiations

Comparable to Angluin's L* in efficiency

Abstract

We present two active learning algorithms for sound deterministic negotiations. Sound deterministic negotiations are models of distributed systems, a kind of Petri nets or Zielonka automata with additional structure. We show that this additional structure allows to minimize such negotiations. The two active learning algorithms differ in the type of membership queries they use. Both have similar complexity to Angluin's L* algorithm, in particular, the number of queries is polynomial in the size of the negotiation, and not in the number of configurations.