reproducibilityindex.ai

Game-Theoretic Resource Allocation for Protecting Large Public Events

Authors: Yue Yin, Bo An, Manish Jain

AAAI 2014 | Conference PDF | Archive PDF | Plain Text | 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 melody1235813@gmail.com Bo An School of Computer Engineering Nanyang Technological University Singapore 639798 boan@ntu.edu.sg Manish Jain Department of Computer Science Virginia Tech Blacksburg, VA 24061 jmanish@cs.vt.edu
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.