Symbolic Planning
Overview
Symbolic planning represents goals, states, and actions explicitly so a system can search for action sequences that achieve a goal. Common approaches use STRIPS or PDDL models with heuristic search, SAT or SMT solving, and hierarchical task planning; other work learns action models and search heuristics. Neuro-symbolic systems connect language and vision models to symbolic planners for robotics, using verification, execution feedback, and replanning to handle real-world tasks. A central challenge is balancing scalable, generalizable planning with reliable grounding and execution when models or observations are incomplete, as evaluations range from IPC benchmarks and puzzles to household robotics.
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