Learning Task Specifications from Demonstrations
Authors: Marcell Vazquez-Chanlatte, Susmit Jha, Ashish Tiwari, Mark K. Ho, Sanjit Seshia
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we demonstrate how learning specifications can help avoid common problems that often arise due to ad-hoc reward composition.Running a fairly unoptimized implementation of Algorithm 2 on the concept class and demonstrations took approximately 95 seconds and resulted in 172 eϕ queries ( 18% of the concept class). |
| Researcher Affiliation | Collaboration | 1 University of California, Berkeley 2 SRI International, Menlo Park |
| Pseudocode | Yes | Algorithm 1 Inference on chains and Algorithm 2 Inference on Partial Orders |
| Open Source Code | No | No explicit statement or link providing access to the source code for the methodology described in this paper was found. |
| Open Datasets | No | The paper uses custom demonstrations shown in Figure 1 for its experiments but does not provide concrete access information (link, DOI, or formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper uses a set of demonstrations for inference but does not specify any explicit training, validation, or test dataset splits. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments are provided in the paper. |
| Software Dependencies | No | The paper mentions using a "SAT solver" and "Binary Decision Diagram (BDD)" but does not specify version numbers for these or any other software dependencies. |
| Experiment Setup | No | The paper describes the scenario and concept class but does not provide specific experimental setup details such as hyperparameters, training configurations, or other system-level settings. |