An Abstraction-Refinement Methodology for Reasoning about Network Games
Authors: Guy Avni, Shibashis Guha, Orna Kupferman
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 guy.avni@ist.ac.at Shibashis Guha The Hebrew University shibashis@cs.huji.ac.il Orna Kupferman The Hebrew University orna@cs.huji.ac.il |
| 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. |