Source note2 min read
Dynamic Resource Allocation for Ensemble Determinization MCTS
Source
arXiv
arxiv.org/abs/2607.13007v1
Source
- Source name: arXiv
- Source kind: paper
- Authors / organization: Jakub Kowalski, Adam Ciężkowski, Artur Krzyżyński, Mark H. M. Winands
- Venue: arXiv: cs.AI
- Published: 2026-07-14
- Collected: 2026-07-15
- URL: http://arxiv.org/abs/2607.13007v1
- PDF: https://arxiv.org/pdf/2607.13007v1
- DOI: UNKNOWN
Plain-Language Brief
The paper proposes enhancements for Ensemble Determinization Monte Carlo Tree Search (MCTS) algorithms, specifically focusing on dynamic resource allocation methods. These methods include Dynamic Number of Determinizations and Dynamic Simulation Allocation, which aim to improve the performance of MCTS in high-uncertainty environments by adjusting the number of determinization trees and simulation budget across them accordingly.
What the Document Says
- The paper is about improving Monte Carlo Tree Search (MCTS) algorithms for high-uncertainty environments.
- Two dynamic resource allocation methods are proposed: Dynamic Number of Determinizations and Dynamic Simulation Allocation.
- These methods aim to improve MCTS performance by adjusting the number of determinization trees and simulation budget.
Key Concepts Mentioned
- Monte Carlo Tree Search
- Dynamic Number of Determinizations
- Dynamic Simulation Allocation
Claims Made by the Document
- The proposed enhancements can yield a statistically significant increase in the algorithm's strength.
Evidence Used
- Testing our proposed enhancements in iteration- and time-based settings showed that particular configurations yield a statistically significant increase in the algorithm's strength.
Methods / Approach
- Simulation-based algorithms
- Ensemble Determinization MCTS
Limitations Stated by the Authors
UNKNOWN
Limitations Noted by the Agent
Agent interpretation — not from the source.
UNKNOWN
Open Questions
UNKNOWN
Notes for Future Q&A
May include interpretation. Labeled where it does.
UNKNOWN
Raw Abstract / Excerpt
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.
