Daniel Kasenberg, Ravenna Thielstrom, Matthias Scheutz |
PosterID:
27
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| 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 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. | |
| Canb | 10/28/2020, 03:00 – 04:00 |
| 10/30/2020, 10:00 – 11:00 |
| Paris | 10/27/2020, 17:00 – 18:00 |
| 10/30/2020, 00:00 – 01:00 |
| NYC | 10/27/2020, 12:00 – 13:00 |
| 10/29/2020, 19:00 – 20:00 |
| LA | 10/27/2020, 09:00 – 10:00 |
| 10/29/2020, 16:00 – 17:00 |
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