Although temporal logic has been touted as a fruitful language for specifying interpretable agent objectives, there has been little emphasis on generating explanations for agents with temporal logic objectives. In this paper, we develop an approach to generating explanations for the behavior of agents planning with several temporal logic objectives. We focus on agents operating in deterministic Markov decision processes (MDPs), and specify objectives using linear temporal logic (LTL). Given an agent planning to maximally satisfy some set of LTL objectives (with an associated preference structure) in a deterministic MDP, we introduce an algorithm for constructing explanations answering both factual and “why” queries, which queries are also specified in LTL.
@inproceedings{kasenberg2020icaps,
title={Generating Explanations for Temporal Logic Planner Decisions},
author={Daniel Kasenberg and Ravenna Thielstrom and Matthias Scheutz},
year={2020},
booktitle={Proceedings of the 30th International Conference on Automated Planning and Scheduling (ICAPS)},
url={https://hrilab.tufts.edu/publications/kasenberg2020icaps.pdf}
doi={10.1609/icaps.v30i1.6740}
}