Acting and Planning Using Operational Models
Authors: Sunandita Patra, Malik Ghallab, Dana Nau, Paolo Traverso7691-7698
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show significant benefits in the efficiency of the acting and planning system. We have implemented and tested our framework on four domains. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Institute for Systems Research, University of Maryland, College Park, USA 2Centre national de la recherche scientifique (CNRS), Toulouse, France 3Fondazione Bruno Kessler (FBK), Povo Trento, Italy |
| Pseudocode | Yes | Algorithm 1: RAE (Refinement Acting Engine) |
| Open Source Code | Yes | Full code is online at https://bitbucket.org/sunandita/raeplan. |
| Open Datasets | No | The paper describes experimental domains (Explorable Environment, Chargeable Robot, Spring Door, Industrial Plant) and test suites but does not provide specific links, DOIs, or citations to publicly available datasets used within these domains. |
| Dataset Splits | No | The paper discusses running problems multiple times but does not specify train, validation, or test dataset splits in terms of percentages, absolute counts, or predefined splits. |
| Hardware Specification | Yes | running on a 2.6 GHz Intel Core i5 processor. |
| Software Dependencies | No | The paper does not mention specific software dependencies with version numbers (e.g., Python, PyTorch, or specific solvers/libraries). |
| Experiment Setup | Yes | We ran experiments with k = 0, 3, 5, 7, 10. In the CR, EE and IP domains we used b = 1, 2, 3 because each task are at most three method instances. In the SD domain, we used b = 1, 2, 3, 4 because it has four methods for opening a door. |