Instructable Intelligent Personal Agent
Authors: Amos Azaria, Jayant Krishnamurthy, Tom Mitchell
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A user study involving email tasks demonstrates that users voluntarily teach LIA new commands, and that these taught commands significantly reduce task completion time. |
| Researcher Affiliation | Collaboration | 1 Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213 2 Allen Institute for Artificial Intelligence, Seattle, WA 98103 |
| Pseudocode | Yes | Pseudocode for lexicon induction is provided as Algorithm 1. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | The paper mentions 'LIA s semantic parser has over 300 lexicon entries, 14 unary rules, and was trained using 150 training examples', but it does not state that this dataset is publicly available or provide a link/citation to it. |
| Dataset Splits | No | The paper mentions training data for the semantic parser ('trained using 150 training examples') and a user study, but it does not specify explicit training, validation, and test dataset splits with percentages or counts for model reproduction. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper describes the software components of LIA (e.g., CCG semantic parser, back-end), but it does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | No | The 'Experimental Setup' section details the user study methodology (tasks, subjects, questionnaires) but does not include specific hyperparameters or system-level training settings for the underlying machine learning model (e.g., learning rate, batch size for the semantic parser). |