Limited Lookahead in Imperfect-Information Games
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Abstract (arXiv)
Limited lookahead has been studied for decades in perfect-information games. We initiate a new direction via two simultaneous deviation points: generalization to imperfect-information games and a game-theoretic approach. We study how one should act when facing an opponent whose lookahead is limited. We study this for opponents that differ based on their lookahead depth, based on whether they, too, have imperfect information, and based on how they break ties. We characterize the hardness of finding a Nash equilibrium or an optimal commitment strategy for either player, showing that in some of these variations the problem can be solved in polynomial time while in others it is PPAD-hard, NP-hard, or inapproximable. We proceed to design algorithms for computing optimal commitment strategies---for when the opponent breaks ties favorably, according to a fixed rule, or adversarially. We then experimentally investigate the impact of limited lookahead. The limited-lookahead player often obtains the value of the game if she knows the expected values of nodes in the game tree for some equilibrium---but we prove this is not sufficient in general. Finally, we study the impact of noise in those estimates and different lookahead depths.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Feb 2019.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 0; 2026: 1.
3 papers.
- 2023Suspicion-Agent: Playing Imperfect Information Games with Theory of Mind Aware GPT-4cited by 67
- 2024Who is Undercover? Guiding LLMs to Explore Multi-Perspective Team Tactic in the Gamecited by 3
- 2026GPU-CFR: 80x Faster Counterfactual Regret Minimization by Compiling the Game to Static Dataflow and CUDA Graph Replaycited by 0
- 2026GPU-CFR: 80x Faster Counterfactual Regret Minimization by Compiling the Game to Static Dataflow and CUDA Graph Replay10 Sep 2026
- 2024Who is Undercover? Guiding LLMs to Explore Multi-Perspective Team Tactic in the Game20 Oct 2024
- 2023Suspicion-Agent: Playing Imperfect Information Games with Theory of Mind Aware GPT-429 Sep 2023