Anticipatory Commonsense with Moving Objects: An ASP-Based Framework for Predictive Reasoning with Interacting Trajectories

A new arXiv paper (2610.20867v1) presents an ASP-based declarative framework for anticipatory commonsense reasoning about interacting motion trajectories, combining an Answer Set Programming model of interactive motion dynamics, semantically-guided prediction of "branching trajectory futures," and an energy-based optimisation model to evaluate, rank and filter anticipations. The authors evaluated the framework on the Argoverse 2 benchmark, where each anticipated trajectory carries an explicit, inspectable event-level hypothesis, unlike end-to-end stochastic prediction.

arXiv:2610.20867v1 Announce Type: new Abstract: We present a general declarative framework for anticipatory reasoning about interactive motion in complex dynamic environments. Our focus is on explainable anticipatory space-time mental model formation at a semantic level to heuristically guide low-level motion computation. Our method is hybrid, modular and elaboration tolerant integrating: 1 An ASP model characterising the dynamics of interactive motion trajectories; 2 Semantically-guided prediction for deriving branching trajectory futures'; and 3 An energy-based {optimisation} model to evaluate, rank and filter anticipations. We evaluate with the Argoverse 2 benchmark, where each anticipated trajectory carries an explicit, inspectable event-level hypothesis, unlike end-to-end stochastic prediction.