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 [1].
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. |