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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Chance-Constrained Probabilistic Simple Temporal Problems
Authors: Cheng Fang, Peng Yu, Brian Williams
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper we present the probabilistic Simple Temporal Network (p STN), a probabilistic formalism for representing temporal problems with bounded risk and a utility over event timing. We introduce a constrained optimisation algorithm for p STNs that achieves compactness and efficiency through a problem encoding in terms of a parameterised STNU and its reformulation as a parameterised STN. We demonstrate through a car sharing application that our chance-constrained approach runs in the same time as the previous probabilistic approach, yields solutions with utility improvements of at least 5% over previous arts, while guaranteeing operation within the specified risk bound. |
| Researcher Affiliation | Academia | Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory 32 Vassar Street, Cambridge, MA 02139 EMAIL |
| Pseudocode | Yes | Algorithm 1: Approximating cc-p STP |
| Open Source Code | No | The paper does not provide any links to open-source code or explicit statements about releasing code. |
| Open Datasets | No | The paper states: |
| Dataset Splits | No | The paper mentions generating 1800 p STNs but does not specify training, validation, or test splits. |
| Hardware Specification | No | The paper mentions |
| Software Dependencies | No | The paper mentions |
| Experiment Setup | Yes | In each scenario we schedule for a 6 hour period, with the number of cars ranging from 1 to 20, each with up to 5 users. For each user, up to three goal locations were generated based on a simplified open source map of Boston. A p STN was generated for each scenario. The traversal activities were modelled as normally distributed uncertain durations, with the means of u Dns determined by length and speed limits of the roads taken, and standard deviations at 5% of the mean. A total of 1800 p STNs were generated. ... For each p STN, we constructed three cc-p STPs, with chance-constraints 10%, 20% and 40%. |