Dynamic Resource Allocation for Ensemble Determinization MCTS

2026-07-14

AI Summary

This work addresses the inefficiency of traditional Monte Carlo Tree Search (MCTS) in adversarial tabletop games with high uncertainty—such as Jaipur, Lost Cities, and Splendor—where fixed determinization strategies hinder optimal use of computational resources. To overcome this limitation, the authors propose a dual-axis dynamic resource allocation mechanism integrated within determinized MCTS: it adaptively adjusts the number of determinization trees while allocating simulations non-uniformly based on knowledge gain, thereby enabling real-time optimization of the computational budget. Experimental results demonstrate that, under identical iteration and time constraints, the proposed approach yields statistically significant improvements in win rates, confirming its effectiveness and superiority in decision-making under uncertainty.

Abstract

Simulation-based algorithms are especially suited for high-uncertainty environments such as adversarial board games with significant elements of randomness and hidden information. In particular, several Monte Carlo Tree Search (MCTS) variants are commonly used in such domains. In this paper, we propose a series of enhancements for Ensemble Determinization MCTS, introducing two axes for dynamic resource allocation. First, Dynamic Number of Determinizations, increases or decreases the number of currently used determinization trees depending on the behavior of so-far search. Second, Dynamic Simulation Allocation, splits the simulation budget nonuniformly across the determinization trees, using simulation-to-simulation decisions to choose the tree with potentially the best knowledge gain. As benchmark domains, we used three popular tabletop games: Jaipur, Lost Cities, and Splendor. Testing our proposed enhancements in iteration- and time-based settings showed that particular configurations yield a statistically significant increase in the algorithm's strength.

Problem

• Dynamic Resource Allocation
• Ensemble Determinization MCTS
• High-uncertainty Environments
• Adversarial Board Games
• Simulation-based Algorithms

Innovation

• Dynamic Resource Allocation
• Ensemble Determinization MCTS
• Monte Carlo Tree Search
• Simulation Budget Allocation
• Adversarial Board Games