planningalgoritmen

Planning algorithms are methods for computing a plan—a sequence of actions that transitions a system from an initial state to a goal state under a given set of rules and constraints. In artificial intelligence and operations research, planning algorithms aim to find an action sequence that achieves objectives while respecting resources, temporal constraints, and other limitations. Plans are typically represented as ordered lists of operators or as partially ordered sets of actions.

Classical planning models the world as a set of states and actions with preconditions and effects. Common representations include STRIPS and the Planning Domain Definition Language (PDDL). Plans can be produced by different approaches: forward search in the state space, backward or regression planning, and partial-order planning. Planning graphs, as in GraphPlan, and heuristic-guided forward search methods (for example, the Fast Forward FF planner) are influential. Heuristic estimators often derive from relaxed problems that ignore delete effects, enabling efficient guidance toward the goal.

Beyond classical AI planning, planning algorithms address time and resource constraints in scheduling, logistics, manufacturing, and robotics. Such problems include resource-constrained project scheduling and temporal planning, where duration, concurrency, and resource availability matter. Computational complexity is high in general: many planning problems are NP-hard, and extended temporal or resource-aware variants can be PSPACE-complete. Nevertheless, domain-independent planners, domain-specific solvers, and hybrid approaches are used in practice.

Applications span autonomous robots, supply chains, production planning, mission planning in aerospace, and game AI. Ongoing research focuses on scalability, probabilistic and contingent planning, learning from experience, and integration with perception and execution in real time.