Research map / Learning theory and algorithms / Submodular and game algorithms
Converging to Team-Maxmin Equilibria in Zero-Sum Multiplayer Games
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Initialize restricted game with CTME-based strategies and terminate early using CTME utility (CISGT vs ISGT) (ablation) | ISG for TMEs · Network security grid, 5x5 and larger | runtime (s) | Helped |
| Use CISGT instead of FullTME (enumerating all pure strategies) | TME computation · Network security grid 5x5 (p=0.8,q=0.6) | runtime (s): FullTME out of memory vs 9.8s | Helped |
| Vanilla-ISG (NE convergence) instead of TME-aware ISG (ablation) | CTME/NE computation in ISG · Network security grid 4x4 (p=0.4,q=0.2) | team utility gap relative to CISGT: 35% gap | Hurt |
| TMSP transformation of CTME (TMSP-Alg) instead of computing TME (ablation) | TME approximation from CTME · Network security grid 4x4 (p=0.4,q=0.2) | team utility gap relative to CISGT: 61% gap | Hurt |
| Vanilla-ISG (no TME-aware operations) in multiplayer games | ISG · Three-player theoretical example | convergence to TME | Hurt |
Closest papers by meaning
- Computing Team-Maxmin Equilibria in Zero-Sum Multiplayer Extensive-Form Games (AAAI 2020 · similarity 0.96)
- Computing Ex Ante Coordinated Team-Maxmin Equilibria in Zero-Sum Multiplayer Extensive-Form Games (AAAI 2021 · similarity 0.93)
- Team-Maxmin Equilibrium: Efficiency Bounds and Algorithms (AAAI 2017 · similarity 0.91)
- The Complexity of Symmetric Equilibria in Min-Max Optimization and Team Zero-Sum Games (NeurIPS 2025 · similarity 0.90)
- Securing Equal Share: A Principled Approach for Learning Multiplayer Symmetric Games (ICML 2025 · similarity 0.88)
- Team-Fictitious Play for Reaching Team-Nash Equilibrium in Multi-team Games (NeurIPS 2024 · similarity 0.88)
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