planning-ai

## What I do - Design AI planning systems - Implement classical planning - Build hierarchical task networks - Handle temporal planning - Create scheduling systems - Solve planning under constraints ## When to use me Use me when: - Building autonomous agents - Task automation and orchestration - Robotics planning - Resource scheduling ## Key Concepts ### Planning Paradigms - **Classical**: Fully observable, deterministic - **Stochastic**: Partially observable, probabilistic - **Temporal**: Time constraints - **Hierarchical**: HTN decomposition - **Preference-based**: Soft constraints ### Planning Example (PDDL) ```pddl ;; Domain definition (define (domain robot-navigation) (:requirements :strips :typing) (:types location robot) (:predicates (at ?r - robot ?l - location) (connected ?l1 ?l2 - location) (holding ?r - robot)) (:action move :parameters (?r - robot ?from ?to - location) :precondition (and (at ?r ?from) (connected ?from ?to)) :effect (and (not (at ?r ?from)) (at ?r ?to))) (:action pick :parameters (?r - robot ?l - location) :precondition (at ?r ?l) :effect (holding ?r))) ;; Problem (define (problem robot-prob1) (:domain robot-navigation) (:objects r1 - robot loc1 loc2 loc3 - location) (:init (at r1 loc1) (connected loc1 loc2) (connected loc2 loc3)) (:goal (at r1 loc3))) ``` ### Python Planning (pyplan) ```python from pyplan import planning # A* Search Planning def plan(start, goal, successors, heuristic): frontier = [(0, start)] came_from = {start: None} cost_so_far = {start: 0} while frontier: _, current = heapq.heappop(frontier) if current == goal: return reconstruct_path(came_from, current) for next_state in successors(current): new_cost = cost_so_far[current] + 1 if next_state not in cost_so_far or new_cost < cost_so_far[next_state]: cost_so_far[next_state] = new_cost priority = new_cost + heuristic(next_state, goal) heapq.heappush(frontier, (priority, next_state)) came_from[next_state] = current return None ```