Generalized Confidence Constraints
Authors: Guillaume Perez, Steve Malalel, Gael Glorian, Victor Jung, Alexandre Papadopoulos, Marie Pelleau, Wijnand Suijlen, Jean-Charles Régin, Arnaud Lallouet
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results obtained on a chemical deliveries problem in factories where the chemicals consumption are uncertain shows the efficiency of the proposed approach. |
| Researcher Affiliation | Collaboration | 1 Huawei Technologie, Boulogne-Billancourt, France 2 Universit e Cˆote d Azur, CNRS, Sophia Antipolis, France |
| Pseudocode | No | The paper describes algorithms and propositions in narrative text but does not include structured pseudocode or algorithm blocks (e.g., a figure or section labeled 'Algorithm'). For example, Proposition 1 describes a rule for consistency enforcement but is not formatted as pseudocode. |
| Open Source Code | No | The paper states, 'The implementation uses our internal constraint-programming solver.' It does not provide concrete access to source code for the described methodology. |
| Open Datasets | No | Two data sets have been created from existing data: one called small, and one called large. ... The paper does not provide concrete access information (link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for these datasets, implying they are not publicly available. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. It only mentions 'our internal constraint-programming solver'. |
| Software Dependencies | No | The paper states, 'The implementation uses our internal constraint-programming solver.' It does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Each delivery j D stores a quantity qj in a container bi B. Each container bi has a maximum capacity Ci. Each container bi will be emptied in parallel of a quantity yi unknown in advance. ... The timeout is set to 10 minutes. ... In the small data set, each delivery has a quantity between 1 and 5. Each container contains some previous content and a max capacity ranging from 25 to 40. For each container, the random variable ranges from 0 to 9. ... In the large data set, each delivery has a quantity between 2 and 8. Each container contains some previous content and a max capacity ranging from 50 to 70. For each container, the random variable ranges from 0 to 17. For both of data sets, the number of deliveries span from 40 to 100. |