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..
An Abstraction-Refinement Methodology for Reasoning about Network Games
Authors: Guy Avni, Shibashis Guha, Orna Kupferman
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results demonstrate the efficiency of the methodology. |
| Researcher Affiliation | Academia | Guy Avni IST Austria EMAIL Shibashis Guha The Hebrew University EMAIL Orna Kupferman The Hebrew University EMAIL |
| Pseudocode | Yes | Our procedure (see Fig. 2 for an overview) |
| Open Source Code | No | The paper states: "Our implementations are in Python, we use the library Networkx [Hagberg et al., 2008] for graph generation and graph algorithms", but it does not provide concrete access to the source code for the methodology described in the paper itself. |
| Open Datasets | No | The paper describes generating random games: "We generate a random game, given the parameters n, w, k IN and p [0, 1]. We use the Erd os-R eyni G(n, p) model to generate the network." This is not a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper describes generating random games but does not specify any training/test/validation dataset splits needed to reproduce the experiment. |
| Hardware Specification | Yes | we ran our experiments on a personal computer, Intel Core i5 quad core 1.75 GHz processor, with 8 GB memory. |
| Software Dependencies | No | Our implementations are in Python, we use the library Networkx [Hagberg et al., 2008] for graph generation and graph algorithms. (No version numbers provided for Python or Networkx.) |
| Experiment Setup | Yes | We generate a random game, given the parameters n, w, k IN and p [0, 1]. We use the Erd os-R eyni G(n, p) model to generate the network. Then, for each edge in the graph, we choose at random a cost in a set {0, . . . , w}. For each player i [k], we choose at random a source vertex si and a reachable target vertex ti. We focus on the number |V | of vertices in the concrete network, the number k of players, and the range |W| of weights on the edges. The number of edges is approximately 1/2 |V |2. We fix two of the parameters and increase the third. |