RESEARCH · RESEARCH · #2093
ASP-based framework for anticipatory commonsense prediction with moving objects (arXiv:2610.20867v1)
This preprint introduces a hybrid, declarative framework that uses Answer Set Programming to form explainable, semantic-level anticipatory models of interacting motion. It combines an ASP dynamics model, semantically guided branching-trajectory prediction, and an energy-based optimisation stage to rank and filter anticipations, and is evaluated on the Argoverse 2 benchmark where each predicted trajectory carries an explicit, inspectable event-level hypothesis.
KEY POINTS
- This preprint introduces a hybrid, declarative framework that uses Answer Set Programming to form explainable, semantic-level anticipatory models of interacting motion.
- It combines an ASP dynamics model, semantically guided branching-trajectory prediction, and an energy-based optimisation stage to rank and filter anticipations, and is evaluated on the Argoverse 2 benchmark where each predicted trajectory carries an explicit, inspectable event-level hypothesis.
- Provides an explainable, semantic anticipatory model for interactive motion that can produce inspectable hypotheses to guide low-level motion planning and improve interpretability for autonomous systems.
WHY IT MATTERS
Provides an explainable, semantic anticipatory model for interactive motion that can produce inspectable hypotheses to guide low-level motion planning and improve interpretability for autonomous systems.