Balancing Explicability and Explanations in Human-Aware Planning
Authors: Tathagata Chakraborti, Sarath Sreedharan, Subbarao Kambhampati
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The empirical evaluations demonstrate the effectiveness of the approach from the robot s perspective, while the study highlight its usefulness in being able to conform to expected normative behavior. |
| Researcher Affiliation | Collaboration | Tathagata Chakraborti1 , Sarath Sreedharan2 and Subbarao Kambhampati2 1IBM Research AI, Cambridge MA 02142 USA 2Arizona State University, Tempe AZ 85281 USA tchakra2@ibm.com, {ssreedh3, rao}@asu.edu |
| Pseudocode | Yes | Algorithm 1 MEGA |
| Open Source Code | Yes | The code is available at https://bit.ly/2XTKHz0. |
| Open Datasets | Yes | We will illustrate this trade-off on modified versions of two popular IPC domains. From the International Planning Competition (IPC) 2011: http://www.plg.inf.uc3m.es/ipc2011-learning/Domains.html |
| Dataset Splits | No | The paper uses standard IPC domains and a custom USAR domain but does not explicitly describe train/validation/test splits or their sizes. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for running the experiments (e.g., CPU/GPU models, memory). |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., programming languages, libraries, or specific solver versions) used in the experiments. |
| Experiment Setup | No | The paper discusses the hyper-parameter α but does not provide concrete details on other experimental setup parameters such as learning rates, batch sizes, optimizers, or specific training configurations. |