Strategy Representation by Decision Trees in Reactive Synthesis

TL;DR

This paper introduces decision trees as a new, more efficient way to represent strategies in graph games with parity objectives, improving over traditional BDDs by providing more succinct and decision-flavor-preserving representations.

Contribution

The work develops novel techniques to adapt decision trees for error-free strategy representation in graph games, enabling entropy-based minimization and practical implementation.

Findings

Decision trees outperform BDDs in strategy representation efficiency.

New techniques allow decision trees to accurately represent entire strategies without errors.

Experimental results demonstrate significant improvements in reactive synthesis problems.

Abstract

Graph games played by two players over finite-state graphs are central in many problems in computer science. In particular, graph games with $\omega$-regular winning conditions, specified as parity objectives, which can express properties such as safety, liveness, fairness, are the basic framework for verification and synthesis of reactive systems. The decisions for a player at various states of the graph game are represented as strategies. While the algorithmic problem for solving graph games with parity objectives has been widely studied, the most prominent data-structure for strategy representation in graph games has been binary decision diagrams (BDDs). However, due to the bit-level representation, BDDs do not retain the inherent flavor of the decisions of strategies, and are notoriously hard to minimize to obtain succinct representation. In this work we propose decision trees for strategy representation in graph games. Decision trees retain the flavor of decisions of strategies and allow entropy-based minimization to obtain succinct trees. However, decision trees work in settings (e.g., probabilistic models) where errors are allowed, and overfitting of data is typically avoided. In contrast, for strategies in graph games no error is allowed, and the decision tree must represent the entire strategy. We develop new techniques to extend decision trees to overcome the above obstacles, while retaining the entropy-based techniques to obtain succinct trees. We have implemented our techniques to extend the existing decision tree solvers. We present experimental results for problems in reactive synthesis to show that decision trees provide a much more efficient data-structure for strategy representation as compared to BDDs.