Variable-Delay Controllability
Authors: Nikhil Bhargava, Christian Muise, Brian Williams
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conclude by providing empirical evaluations of the quality of variable-delay controllability results as compared to approximations that use fixed delays to model the same problems. ... We provide an efficient O(n3) variable-delay controllability checker and show how to create an execution strategy for variable-delay controllability problems. To our knowledge, these essential capabilities are absent from existing controllability checking algorithms. ... 5 Empirical Evaluation ... We generated 1000 different STNUs and compared the variable-delay controllability results to the different fixeddelay controllability approaches (Table 1). |
| Researcher Affiliation | Academia | Nikhil Bhargava, Christian Muise, Brian Williams Massachusetts Institute of Technology {nkb, cjmuise, williams}@mit.edu |
| Pseudocode | Yes | Algorithm 1: Algorithm for converting a variable-delay controllability problem to a fixed-delay controllability one. |
| Open Source Code | No | The paper does not include any statement about making its source code publicly available or provide a link to a code repository for the methodology described. |
| Open Datasets | No | The paper states, "We constructed a set of randomly generated STNUs." and describes the parameters of this generation, but it does not provide concrete access information (e.g., a link, DOI, or specific repository) for this generated dataset to be publicly available. |
| Dataset Splits | No | The paper describes how the synthetic data was generated and evaluated, but it does not specify any explicit training, validation, or test dataset splits (e.g., percentages or counts) as would be typical for machine learning models. The 1000 generated STNUs are used for the empirical evaluation of the checker. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU, memory, or cloud instances) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9, CPLEX 12.4) that would be needed to replicate the experiment. |
| Experiment Setup | Yes | Each STNU had 10 contingent links with lowerbound 0 and an integer upper-bound uniformly chosen between 1 and 4. Each contingent link had a variable-delay function with a lower-bound of 0 and upper-bound chosen from the exponential distribution f(t) = λe λt with λ = 0.5. For each pair of contingent link endpoints, we established a requirement link between them with probability 1 40. Each requirement link had a lower-bound of 0 and an integer upperbound uniformly chosen between 1 and 4. ... We employed three different strategies for our fixed-delay approximations: γ(xe) = γ (xe), γ(xe) = γ + γ+ / 2 , and γ(xe) = γ+(xe). |