Visualizations for an Explainable Planning Agent
Abstract
In this demonstration, we report on the visualization capabilities of an Explainable AI Planning (XAIP) agent that can support human-in-the-loop decision-making. Imposing transparency and explainability requirements on such agents is crucial for establishing human trust and common ground with an end-to-end automated planning system. Visualizing the agent's internal decision making processes is a crucial step towards achieving this. This may include externalizing the "brain" of the agent: starting from its sensory inputs, to progressively higher order decisions made by it in order to drive its planning components. We demonstrate these functionalities in the context of a smart assistant in the Cognitive Environments Laboratory at IBM's T.J. Watson Research Center.
Cite
Text
Chakraborti et al. "Visualizations for an Explainable Planning Agent." International Joint Conference on Artificial Intelligence, 2018. doi:10.24963/IJCAI.2018/849Markdown
[Chakraborti et al. "Visualizations for an Explainable Planning Agent." International Joint Conference on Artificial Intelligence, 2018.](https://mlanthology.org/ijcai/2018/chakraborti2018ijcai-visualizations/) doi:10.24963/IJCAI.2018/849BibTeX
@inproceedings{chakraborti2018ijcai-visualizations,
title = {{Visualizations for an Explainable Planning Agent}},
author = {Chakraborti, Tathagata and Fadnis, Kshitij P. and Talamadupula, Kartik and Dholakia, Mishal and Srivastava, Biplav and Kephart, Jeffrey O. and Bellamy, Rachel K. E.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2018},
pages = {5820-5822},
doi = {10.24963/IJCAI.2018/849},
url = {https://mlanthology.org/ijcai/2018/chakraborti2018ijcai-visualizations/}
}