Htn Planning Representation Languages and Tools

Planning systems can be put into two classes: domain-independent planners and domain-dependent planners. Domain-dependent planners concentrate on using domain heuristics to encourage eecient search. For a domain-dependent planner, the domain contains heuristics algorithm for the speciied problem to make an eecient planning, so the planner might not available from one application to another one. Domain-independent planning is building general-purpose planning systems that can process the speciication of an application domain and then generate solutions to planning problems in that domain. A classical AI planning system is domain-independent. The planner uses a language to describe the planning problem as input, and then the planner produces the actions sequence as result. Building domain-independent planning systems is very challenging. The language used for writing domains and problem speciications should have a precise semantics should be structured to allow \natural" encoding for a range of applications. The precise semantics is very important in developing correct and eecient algorithms for solving problems written in that language, and also facilitates writing correct domain speciications 1]. The construction of the domain model needed for an application has been shown in practice to be very slow and laborious, requiring expertise not only in the application domain but in the detailed working of the planning system. It is generally agreed that thèhand-crafted' approach to domain model construction currently in use must be replaced by stronger and more consistent methods which can compete successfully with more conventional approaches in terms of software lifecycle costs 10]. For example, NASA has introduced a compositional, model-based programming language that supports the speciication of 1