Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Pruning Game Tree by Rollouts
Authors: Bojun Huang
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we show that the α-β algorithm and its successor MT-SSS*, as two classic minimax search algorithms, can be implemented as rollout algorithms, a generic algorithmic paradigm widely used in many domains. Specifically, we define a family of rollout algorithms, in which the rollout policy is restricted to select successor nodes only from a subset of the children list. We show that any rollout policy in this family (either deterministic or randomized) is guaranteed to evaluate the game tree correctly with a finite number of rollouts. Moreover, we identify simple rollout policies in this family that implement α-β and MT-SSS*. |
| Researcher Affiliation | Industry | Bojun Huang Microsoft Research EMAIL |
| Pseudocode | Yes | Algorithm 1: The α-β algorithm enhanced with storage.; Algorithm 2: The MT-SSS* algorithm.; Algorithm 3: A family of rollout algorithms.; Algorithm 4: A variant of the alphabeta procedure, which returns a pair of value bounds. |
| Open Source Code | No | The paper does not provide a specific link to source code for its methodology or explicitly state that the code is publicly available. It only links to the paper itself in the references. |
| Open Datasets | No | The paper is theoretical and does not describe empirical experiments involving datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments with dataset splits for training, validation, and testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental setup that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |