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

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.

Accepted at ACS 2026 / Advances in Cognitive Systems 2026. (to appear)

Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)

arXiv:2610.20867 [cs.AI]

(or arXiv:2610.20867v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.20867 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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