arXiv:2403. 19883v2 Announce Type: replace Abstract: Fully-observable non-deterministic (FOND) planning is at the core of artificial intelligence planning with uncertainty.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2607. 21414v1 Announce Type: new Abstract: In automated planning, logical regression is an operation that returns the most general condition necessary for an action to achieve a particular formula.
The paper develops a geometric theory of decision boundaries for structured Markov Decision Processes, treating the geometry induced by optimal policies as the key analytical object. It shows that, under structural regularity, this geometry yields the minimal representation needed for policy reconstruction and dictates the statistical and computational complexity of the reconstruction problem. The authors introduce intrinsic notions of boundary and decision complexity, derive information-theoretic measures of decision compression, and provide statistical guarantees for boundary estimation and policy reconstruction from black-box queries, supported by controlled numerical experiments.
arXiv:2601. 22211v2 Announce Type: replace Abstract: Reinforcement learning (RL) with combinatorial action spaces remains challenging because feasible action sets are exponentially large and governed by complex feasibility constraints, making direct policy parameterization impractical.
arXiv:2607. 05359v1 Announce Type: new Abstract: Planning under uncertainty in continuous domains is essential for autonomous systems, yet computationally demanding.
arXiv:2604. 12474v3 Announce Type: replace-cross Abstract: In many robotic tasks, agents must traverse a sequence of spatial regions to complete a mission.
arXiv:2606. 02438v1 Announce Type: new Abstract: Learned heuristics have recently become a competitive alternative to traditional domain-independent heuristics for satisficing planning.