Improvement in Small Progress Measures

TL;DR

This paper improves the Small Progress Measures algorithm for parity games by enabling the derivation of winning strategies for both players in a single pass, significantly reducing computational complexity.

Contribution

The authors introduce a novel operational interpretation of the least progress measure and modify the algorithm to compute both players' strategies simultaneously.

Findings

Reduces strategy computation time to O(dm.(n/floor(d/2))^floor(d/2))

Provides a new operational interpretation of the least progress measure

Achieves single-pass derivation of winning strategies for both players

Abstract

Small Progress Measures is one of the classical parity game solving algorithms. For games with n vertices, m edges and d different priorities, the original algorithm computes the winning regions and a winning strategy for one of the players in O(dm.(n/floor(d/2))^floor(d/2)) time. Computing a winning strategy for the other player requires a re-run of the algorithm on that player's winning region, thus increasing the runtime complexity to O(dm.(n/ceil(d/2))^ceil(d/2)) for computing the winning regions and winning strategies for both players. We modify the algorithm so that it derives the winning strategy for both players in one pass. This reduces the upper bound on strategy derivation for SPM to O(dm.(n/floor(d/2))^floor(d/2)). At the basis of our modification is a novel operational interpretation of the least progress measure that we provide.