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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Game-Theoretic Resource Allocation for Protecting Large Public Events
Authors: Yue Yin, Bo An, Manish Jain
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that both SCOUT-A and SCOUT-C significantly outperform other existing strategies. |
| Researcher Affiliation | Academia | Yue Yin The Key Lab of Intelligent Information Processing, ICT, CAS University of Chinese Academy of Sciences Beijing 100190, China EMAIL Bo An School of Computer Engineering Nanyang Technological University Singapore 639798 EMAIL Manish Jain Department of Computer Science Virginia Tech Blacksburg, VA 24061 EMAIL |
| Pseudocode | Yes | Algorithm 1: SCOUT-A ... Algorithm 2: SCOUT-C |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | All experiments are averaged over 50 sample games. Unless otherwise specified, we use 4 targets, 5 security resources, te = 10, λ = 1 to describe the marginal utility of an extra security resource... We randomly choose a time period in [0, te] in which a value function is non-zero. |
| 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 the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | Yes | We use KNITRO version 8.0.0 to solve SCOUT-D. |
| Experiment Setup | Yes | All experiments are averaged over 50 sample games. Unless otherwise specified, we use 4 targets, 5 security resources, te = 10, λ = 1 to describe the marginal utility of an extra security resource. |