English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Seamlessly integrating rules in Learning-from-Demonstrations (LfD) policiesis a critical requirement to enable the real-world deployment of AI agents.Recently, Signal Temporal Logic (STL) has been shown to be an effectivelanguage for encoding rules as spatio-temporal constraints. This work usesMonte Carlo Tree Search (MCTS) as a means of integrating STL specification intoa vanilla LfD policy to improve constraint satisfaction. We propose augmentingthe MCTS heuristic with STL robustness values to bias the tree search towardsbranches with higher constraint satisfaction. While the domain-independentmethod can be applied to integrate STL rules online into any pre-trained LfDalgorithm, we choose goal-conditioned Generative Adversarial Imitation Learningas the offline LfD policy. We apply the proposed method to the domain ofplanning trajectories for General Aviation aircraft around a non-toweredairfield. Results using the simulator trained on real-world data showcase 60%improved performance over baseline LfD methods that do not use STL heuristics.

Follow The Rules: Online Signal Temporal Logic Tree Search for Guided Imitation Learning in Stochastic Domains