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].
Optimal Interdiction of Urban Criminals with the Aid of Real-Time Information
Authors: Youzhi Zhang, Qingyu Guo, Bo An, Long Tran-Thanh, Nicholas R. Jennings1262-1269
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 7 Experimental Evaluation We demonstrate the ef๏ฌciency of our algorithms through numerical experiments. We use CPLEX (version 12.6) to solve the linear program. All computations are performed on a machine with a 3.2GHz quad core CPU and 16GB memory. All results are averaged over 30 randomly generated instances. All random planar graphs are generated by the grid model with random edges (Peng et al. 2014), which samples an L ห W square grid where horizontal/vertical edges between neighbors are generated with probability p, and diagonal ones with q. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Nanyang Technological University, Singapore 2Department of Electronics and Computer Science, University of Southampton, UK 3Departments of Computing and Electrical and Electronic Engineering, Imperial College, UK |
| Pseudocode | Yes | Algorithm 1: IGRS and Algorithm 2: BRps,bsq |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | We also evaluate our approach on the road network of the whole Manhattan island of New York as shown in Figure 2(j). In this network, there are 702 nodes with 1,216 links extracted from OSM (www.openstreetmap.org). |
| Dataset Splits | No | The paper describes generating random graph instances and selecting initial positions, but does not specify training, validation, or test dataset splits (e.g., 80/10/10 split) or cross-validation setup for any dataset. |
| Hardware Specification | Yes | All computations are performed on a machine with a 3.2GHz quad core CPU and 16GB memory. |
| Software Dependencies | Yes | We use CPLEX (version 12.6) to solve the linear program. |
| Experiment Setup | Yes | By default, L W 5, pp, qq p0.5, 0.1q, horizon tmax L, |Ve| 10, and m 4. |