tasks. This does not necessarily involve state variables, although in more realistic applications state variables simplify the description of task networks. Algorithms for planning Classical planning forward chaining state space search, possibly enhanced with heuristics backward chaining search, possibly enhanced by the use of state constraints (see STRIPS, graphplan) partial-order planning Reduction to other problems reduction to the propositional satisfiability problem (satplan). reduction to Model checking - both are essentially problems of traversing state spaces, and the classical planning problem corresponds to a subclass of model checking problems. Temporal planning Temporal planning can be solved with methods similar to classical planning. The main difference is, because of the possibility of several, temporally overlapping actions with a duration being taken concurrently, that the definition of a state has to include information about the current absolute time and how far the execution of each active action has proceeded. Further, in planning with rational or real time, the state space may be infinite, unlike in classical planning or planning with integer time. Temporal planning is closely related to scheduling problems when uncertainty is involved and can also be understood in terms of timed automata. The Simple Temporal Network with Uncertainty (STNU) is a scheduling problem which involves controllable actions, uncertain events and temporal constraints. Dynamic Controllability for such problems is a type of scheduling which requires a temporal planning strategy to activate controllable actions reactively as uncertain events are observed so that all constraints are guaranteed to be satisfied. Probabilistic planning Probabilistic planning can be solved with iterative methods such as value iteration and policy iteration, when the state space is sufficiently small. With partial observability, probabilistic planning is similarly solved with iterative methods, but using a representation of the value functions defined for the space of beliefs instead of states. Preference-based planning In preference-based planning, the objective is not only to produce a plan but also to satisfy user-specified preferences. A difference to the more common reward-based planning, for example corresponding to MDPs, preferences don't necessarily have a precise numerical value. Conditional planning Deterministic planning was introduced with the STRIPS planning system, which is a hierarchical planner. Action names are ordered in a sequence and this is a plan for the robot. Hierarchical planning can be compared with an automatic generated behavior tree. The disadvantage is, that a normal behavior tree is not so expressive like a computer program. That means, the