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..
All-Pay Bidding Games on Graphs
Authors: Guy Avni, Rasmus Ibsen-Jensen, Josef Tkadlec1798-1805
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also implement it, show that it performs well, and suggests interesting properties of these games. ... Our experiments show that the difference between upperand lower-bounds is small, thus we conclude that the algorithm supplies a good approximation to the value function. The experiments verify our theoretical findings. |
| Researcher Affiliation | Academia | 1IST Austria 2Liverpool University |
| Pseudocode | Yes | Figure 3: An FPTAS for finding upperand lower-bounds on the values for every initial budget ratio. (Contains pseudocode for Approx-Values algorithm) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described (no specific repository link or explicit code release statement). |
| Open Datasets | No | The paper discusses applying algorithms to known game structures like Tic-tac-toe and defined "race games," but it does not provide access information or citations for a dataset in the traditional machine learning sense, nor does it specify train/test/validation splits for any data. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce data partitioning for validation. |
| Hardware Specification | No | The paper only states that the algorithm is "run on a personal computer," which is not a specific hardware detail (no GPU/CPU models, memory amounts, or detailed computer specifications). |
| Software Dependencies | No | The paper mentions implementation in "Python" but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | In our experiments, we choose ϵ = 0.01. |