Solving Qualitative Multi-Objective Stochastic Games

2026-02-13

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Security and Privacy: Large-scale security measurements

Abstract

Many problems in compositional synthesis and verification of multi-agent systems -- such as rational verification and assume-guarantee verification in probabilistic systems -- reduce to reasoning about two-player multi-objective stochastic games. This motivates us to study the problem of characterizing the complexity and memory requirements for two-player stochastic games with Boolean combinations of qualitative reachability and safety objectives. Reachability objectives require that a given set of states is reached; safety requires that a given set is invariant. A qualitative winning condition asks that an objective is satisfied almost surely (AS) or (in negated form) with non-zero (NZ) probability. We study the determinacy and complexity landscape of the problem. We show that games with conjunctions of AS and NZ reachability and safety objectives are determined, and determining the winner is PSPACE-complete. The same holds for positive boolean combinations of AS reachability and safety, as well as for negations thereof. On the other hand, games with full Boolean combinations of qualitative objectives are not determined, and are NEXPTIME-hard. Our hardness results show a connection between stochastic games and logics with partially-ordered quantification. Our results shed light on the relationship between determinacy and complexity, and extend the complexity landscape for stochastic games in the multi-objective setting.

Problem

• stochastic games
• multi-objective
• qualitative objectives
• determinacy
• complexity

Innovation

• stochastic games
• qualitative objectives
• multi-objective verification
• determinacy
• PSPACE-completeness