Concurrent Permissive Strategy Templates

2026-01-20

AI Summary

This work addresses the challenge of compactly and compositionally representing stochastic winning strategies that support runtime adaptation in concurrent games—a limitation that hinders their application in cyber-physical systems. The paper introduces Concurrent Strategy Templates (ConSTels), which extend strategy templates to concurrent games for the first time, enabling compact encoding of infinitely many stochastic winning strategies under safety, Büchi, and co-Büchi objectives. ConSTels facilitate incremental synthesis over combined objectives and allow dynamic adjustment of action probabilities based on observed adversary behavior, balancing formal correctness with performance optimization. Experimental evaluation using a prototype tool demonstrates the effectiveness of ConSTels in both strategy synthesis efficiency and runtime adaptability, highlighting their potential for real-world deployment in cyber-physical systems.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Multiagent Systems: Adversarial Agents
• Game Theory and Economic Paradigms: Cooperative Game Theory

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Two-player games on finite graphs provide a rigorous foundation for modeling the strategic interaction between reactive systems and their environment. While concurrent game semantics naturally capture the synchronous interactions characteristic of many cyber-physical systems (CPS), their adoption in CPS design remains limited. Building on the concept of permissive strategy templates (PeSTels) for turn-based games, we introduce concurrent (permissive) strategy templates (ConSTels) -- a novel representation for sets of randomized winning strategies in concurrent games with Safety, B\"uchi, and Co-B\"uchi objectives. ConSTels compactly encode infinite families of strategies, thereby supporting both offline and online adaptation. Offline, we exploit compositionality to enable incremental synthesis: combining ConSTels for simpler objectives into non-conflicting templates for more complex combined objectives. Online, we demonstrate how ConSTels facilitate runtime adaptation, adjusting action probabilities in response to observed opponent behavior to optimize performance while preserving correctness. We implemented ConSTel synthesis and adaptation in a prototype tool and experimentally show its potential.

Problem

• concurrent games
• strategy representation
• reactive systems
• cyber-physical systems
• winning strategies

Innovation

• Concurrent Strategy Templates
• Permissive Strategies
• Randomized Winning Strategies
• Incremental Synthesis
• Runtime Adaptation