arXiv:2605.13282v2 Announce Type: replace-cross Abstract: Classical planners can effectively solve very large deterministic MDPs represented in STRIPS or PDDL where states are sets of atoms over objects and relations, and lifted action schemas add or delete these atoms. This compact representation yields strong search heuristics and provides an ideal setting for structural generalization, since lifted relations and action schemas give rise to infinitely many domain instances. A central challenge is to learn these relations and action schemas from data, and recent approaches have addressed this
The continuous advancements in AI research are pushing the boundaries of autonomous systems, making robust learning of action schemas a critical next step for intelligent agents.
This research addresses a core challenge in AI by enabling more effective learning of planning principles, which is crucial for building adaptable and generalizable AI systems.
The ability to learn lifted action schemas differentiably could significantly improve the autonomy and adaptability of classical planners, leading to AI systems that can infer complex behaviors from data more efficiently.
- · AI/ML Researchers
- · Robotics Developers
- · Automation Software Providers
- · Fixed-logic automation systems
- · Manual planning processes
AI systems will become more capable of learning and adapting their operational plans from diverse datasets.
This capability could accelerate the development of more sophisticated AI agents for various real-world applications.
Increased autonomy in planning could reduce the human oversight required for complex AI operations, potentially impacting labor across various sectors.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG