Complexity results for HTN planning

  title={Complexity results for HTN planning},
  author={K. Erol and J. Hendler and D. Nau},
  journal={Annals of Mathematics and Artificial Intelligence},
Most practical work on AI planning systems during the last fifteen years has been based on Hierarchical Task Network (HTN) decomposition, but until now, there has been very little analytical work on the properties of HTN planners. This paper describes how the complexity of HTN planning varies with various conditions on the task networks, and how it compares to STRIPS-style planning. 
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1 Reasoning about Actions and Planning 2 Basic Assumptions and Limitations 3 SIPE and Its Representations 4 Hierarchical Planning as Differing Abstraction Levels 5 Constraints 6. The Truth CriterionExpand
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A set of restrictions which defines the relationships between a non‐primitive action and its set of subactions guarantee improved efficiency for hierarchical planning and also provides algorithms for preprocessing the planning knowledge of a hierarchical planner. Expand
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Based on this syntax and semantics, an algorithm for HTN planning is defined and it is proved that it is sound and complete and strictly more expressive than STRIPS-style planning according to those definitions. Expand
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The planner (NONLIN) and the Task Formalism (TF) used to hierarchically specify a domain are described, which can aid in the generation of project networks. Expand
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A new information structure is described, called the procedural net, that represents a plan as a partial ordering of actions with respec to time, so that a problem-solving system using this representation can deal easily and directly with problems that are otherwise very difficult to solve. Expand
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A general purpose automated planner/scheduler is described which generates parallel plans to achieve goals with imposed time con-straints. Both durations and start time windows may be specified forExpand
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Abstract I analyze the computational complexity of extended propositional STRIPS planning, i.e., propositional STRIPS planning augmented with a propositional domain theory for inferring additionalExpand
Complexity Results for Planning
There are few results that provide clear dividing lines between tractable and in tractable planning, and below, a few of these dividing lines are clarified by analyzing the computational complexity of a planning problem and a variety of restricted versions, some of which are tractable. Expand