Active Learning within Constrained Environments through Imitation of an Expert Questioner
Authors: Kalesha Bullard, Yannick Schroecker, Sonia Chernova
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments are conducted on a concept learning task to test generalization of the proposed algorithm to different environmental conditions and analyze how time and resource constraints impact efficacy of solving the learning problem. |
| Researcher Affiliation | Academia | Kalesha Bullard , Yannick Schroecker and Sonia Chernova Georgia Institute of Technology {ksbullard, yannickschroecker}@gatech.edu, chernova@cc.gatech.edu |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link for the availability of its source code. |
| Open Datasets | Yes | This task was extracted from the University of Washington RGBD dataset of common household objects [Lai et al., 2011]. |
| Dataset Splits | Yes | Each of the training and test task samples are 80 images and 40 images respectively for the prepare-lunch task and 3200 images and 800 images respectively for the pack-lunchbox task. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, memory specifications). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | All action types were assigned an a-priori cost, which is 2 for demo queries and feature subset queries, 1 for label queries, and 0 for no query. For training of DT-task-env decision feature weights, an expert questioner was given a very constrained query budget (15) and time allocation (30 turns) to ground the prepare-lunch task concepts. During IRL training, the maximum number of iterations was set to 100, and we selected the set of weights w that performed best on the validation set. |