Resolving Over-Constrained Conditional Temporal Problems Using Semantically Similar Alternatives
Authors: Peng Yu, Jiaying Shen, Peter Z. Yeh, Brian Williams
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | When evaluated empirically on a range of urban trip planning scenarios, CDSR demonstrates a substantial improvement in flexibility compared to temporal relaxation only approaches. To evaluate the usefulness of CDSR in such scenarios, we conducted a user study using the personal assistant built with the algorithm. |
| Researcher Affiliation | Collaboration | MIT yupeng@mit.edu; Jiaying Shen and Peter Z. Yeh, Nuance Communications, Inc. {Jiaying.Shen,Peter.Yeh}@nuance.com; Brian Williams, MIT williams@mit.edu |
| Pseudocode | Yes | Algorithm 1: An overview of the CDSR algorithm; Algorithm 2: Function EXPANDONCONFLICT; Algorithm 3: Function EXPANDDOMAINRELAXATION |
| Open Source Code | No | The paper refers to the Word2Vec tool's code (Word2Vec, 2013) but does not state that the code for CDSR itself is open-source or provided. |
| Open Datasets | Yes | The vector model of CDSR is trained by the continuous skip-gram algorithm in the Word2Vec package with a Google News dataset [Word2Vec, 2013]. |
| Dataset Splits | No | The paper discusses scenarios and a user study but does not provide specific training, validation, or test splits for any dataset used for the main experimental evaluation of CDSR. |
| Hardware Specification | No | The paper mentions that components were deployed on 'separate servers' but does not provide specific hardware details such as CPU/GPU models or memory. |
| Software Dependencies | No | The paper mentions the 'Word2Vec package' but does not provide a specific version number for it or any other software dependencies. |
| Experiment Setup | No | The paper describes the user study setup and the algorithm but does not provide specific experimental setup details such as hyperparameters or system-level training settings for the CDSR algorithm itself. |