From the 2 of 7 linked papers with an AI index.
7 papers
ICM Out! Better Tournament Strategy from Computed Continuations, vs. Solvers and LLMs
Boning Li, Longbo Huang
The Independent Chip Model (ICM) converts tournament chips into reference prize equity, and policies are routinely constructed against those values. Because ICM reads only stack si…
Solver-Guided Reasoning for Mixed-Equilibrium Strategies
Han Wang, Philippe Beardsell, Boning Li +4
Reasoning in large language models (LLMs) is often grounded in human text, human demonstrations, and human-generated rationales. For equilibrium reasoning in complex games, however…
AV-AIVAT: 74x Cheaper Agent Evaluation with Certified Anytime-Valid Stopping in Imperfect-Information Games
Boning Li, Yu Chen, Longbo Huang
Deciding which of two agents is stronger means playing games until skill outweighs luck, and every game costs money, model inference, or expert time. Since the number of games need…
Agents That Certify Their Own Exploits: Confidence-Scheduled Restricted Responses for Safe Opponent Exploitation
Boning Li, Longbo Huang
The paper introduces confidence‑scheduled restricted responses (CS‑RNR), a method that lets an agent safely exploit a flawed opponent in two‑player zero‑sum imperfect‑information g…
Correlated Chance Sampling for Monte Carlo Counterfactual Regret Minimization
Boning Li, Yu Chen, Longbo Huang
The paper proposes Correlated Chance Sampling (CCS-MCCFR), a modification to Monte Carlo Counterfactual Regret Minimization that uses persistent randomized streams to reduce sampli…
PokerSkill: LLMs Can Play Expert-Level Poker without Training or Solvers
Boning Li, Baoxiang Wang, Longbo Huang
Poker is a landmark challenge for artificial intelligence. The dominant approach relies on equilibrium solvers built on counterfactual regret minimization, requiring millions of co…