Modular Attractor Acceleration in Infinite-State Games (Full Version)

2026-01-20

AI Summary

This work addresses the challenge of non-termination in symbolic attractor computation for infinite-state games, which often arises due to the lack of expressive and effective acceleration mechanisms. To overcome the limited expressiveness of existing approaches, the paper proposes a modular framework for constructing acceleration parameters by composing elementary acceleration operators into complex strategies. Furthermore, it introduces a generalization-based summarization technique that enables reuse of computed accelerations across different contextual configurations. This approach substantially enhances both the expressiveness and scalability of acceleration mechanisms. When integrated with symbolic fixed-point computation, the method significantly improves the efficiency of solving infinite-state games in reactive synthesis tasks.

Technology Category

• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: Symbolic Representations

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Infinite-state games provide a framework for the synthesis of reactive systems with unbounded data domains. Solving such games typically relies on computing symbolic fixpoints, particularly symbolic attractors. However, these computations may not terminate, and while recent acceleration techniques have been proposed to address this issue, they often rely on acceleration arguments of limited expressiveness. In this work, we propose an approach for the modular computation of acceleration arguments. It enables the construction of complex acceleration arguments by composing simpler ones, thereby improving both scalability and flexibility. In addition, we introduce a summarization technique that generalizes discovered acceleration arguments, allowing them to be efficiently reused across multiple contexts. Together, these contributions improve the efficiency of solving infinite-state games in reactive synthesis, as demonstrated by our experimental evaluation.

Problem

• infinite-state games
• symbolic attractors
• acceleration arguments
• reactive synthesis
• non-termination

Innovation

• modular acceleration
• infinite-state games
• symbolic attractors
• summarization
• reactive synthesis