Interactively Learning a Blend of Goal-Based and Procedural Tasks
Authors: Aaron Mininger, John Laird
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that our approach can learn a variety of goal-oriented and procedural tasks from a single example and is robust to different amounts of domain knowledge. |
| Researcher Affiliation | Academia | Aaron Mininger, John E. Laird University of Michigan 2260 Hayward Street Ann Arbor, MI 48109-2121 {mininger, laird}@umich.edu |
| Pseudocode | Yes | Algorithm 1 Policy Learning |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes using a "simulated version of the mobile robot in a multi-room office environment" but does not provide concrete access information (link, DOI, repository, or formal citation) for a publicly available dataset or the simulated environment's data. |
| Dataset Splits | No | The paper describes learning from single examples and generalizing to new variations but does not provide specific dataset split information (percentages, counts, or predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper mentions deploying the agent on "a tabletop arm, a 4-wheeled mobile robot, and the Fetch robot with a 7 DOF arm, as well as simulated versions of those robots," but it does not provide specific hardware details like GPU/CPU models or memory used for running experiments. |
| Software Dependencies | No | The paper mentions the "Soar cognitive architecture" and "Embodied Construction Grammar" as foundational software, but it does not provide specific version numbers for these or any other ancillary software components. |
| Experiment Setup | No | The paper describes the interactive learning process and how the agent acquires knowledge for tasks, but it does not provide specific experimental setup details such as hyperparameters, training configurations, or system-level settings. |