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..
Using Response Functions to Measure Strategy Strength
Authors: Trevor Davis, Neil Burch, Michael Bowling
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of this technique in Leduc Hold em against opponents that use the UCT Monte Carlo tree search algorithm. |
| Researcher Affiliation | Academia | Trevor Davis and Neil Burch and Michael Bowling EMAIL Department of Computing Science University of Alberta Edmonton, AB, Canada T6G 2EG |
| Pseudocode | No | The paper describes algorithms like CFR-f and UCT, but it does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., a link or explicit statement of release) to open-source code for the described methodology. |
| Open Datasets | No | The paper describes using the game 'Leduc Hold em' as a domain for experiments and references a paper for its full details, but it does not provide concrete access information (link, DOI, repository, or explicit statement of public availability) for a dataset used for training. |
| Dataset Splits | No | The paper does not specify dataset splits (e.g., percentages, counts) for training, validation, or testing. It mentions averaging results over '100 independent runs of UCT'. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper mentions algorithms like UCT and CFR, but it does not provide specific version numbers for any software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | For each iteration t of CFR-UCT, we ran a CFR update for the CFR-agent to create strategy σt 1, then we used UCT to train a response to σt 1. On each iteration the UCT-agent created an entirely new game tree, so the response depended only on σt 1. We gave the UCT-agent k iterations of the UCT algorithm, each of which correspond to one sample of σt 1, where k is a parameter of CFR-UCT. |