Both search-based and translation-based planning systems usually operate on grounded representations of the problem. Planning models, however, are commonly defined using lifted description languages. Thus, planning systems usually generate a grounded representation of the lifted model as a preprocessing step. For HTN planning models, only one method to ground lifted models has been published so far. In this paper we present a new approach for grounding HTN planning problems that produces smaller groundings in a shorter timespan than the previously published method
For HTN planning, we formally characterize and classify four kinds of problem spaces in which each n...
We describe HTN-MAKER, an algorithm for learning hier-archical planning knowledge in the form of dec...
Translation-based approaches to planning allow for solving problems in complex and expressive formal...
International audienceMany Artificial Intelligence techniques have been developed for intelligent an...
Planning problems are usually modeled using lifted representations, they specify predicates and acti...
International audienceMany planning techniques have been developed to allow autonomous systems to ac...
Hierarchical Task Network (HTN) planning is the problem of decomposing an initial task into a sequen...
One difficulty with existing theoretical work on HTN planning is that it does not address some of th...
Landmarks (LMs) are state features that need to be made true or tasks that need to be contained in e...
Current classical planners are very successful in finding (nonoptimal) plans, even for large plannin...
Hierarchical Task Network (HTN) planning is a formalism that can express constraints which cannot ea...
To apply hierarchical task network (HTN) plan-ning to real-world planning problems, one needs to enc...
Keeping planning problems as small as possible is a must in order to cope with complex tasks and env...
HTN planning combines actions that cause state transition with grammar-like decomposition of compoun...
Hierarchical Task Networks (HTN) planning uses a decomposition process guided by domain knowledge to...
For HTN planning, we formally characterize and classify four kinds of problem spaces in which each n...
We describe HTN-MAKER, an algorithm for learning hier-archical planning knowledge in the form of dec...
Translation-based approaches to planning allow for solving problems in complex and expressive formal...
International audienceMany Artificial Intelligence techniques have been developed for intelligent an...
Planning problems are usually modeled using lifted representations, they specify predicates and acti...
International audienceMany planning techniques have been developed to allow autonomous systems to ac...
Hierarchical Task Network (HTN) planning is the problem of decomposing an initial task into a sequen...
One difficulty with existing theoretical work on HTN planning is that it does not address some of th...
Landmarks (LMs) are state features that need to be made true or tasks that need to be contained in e...
Current classical planners are very successful in finding (nonoptimal) plans, even for large plannin...
Hierarchical Task Network (HTN) planning is a formalism that can express constraints which cannot ea...
To apply hierarchical task network (HTN) plan-ning to real-world planning problems, one needs to enc...
Keeping planning problems as small as possible is a must in order to cope with complex tasks and env...
HTN planning combines actions that cause state transition with grammar-like decomposition of compoun...
Hierarchical Task Networks (HTN) planning uses a decomposition process guided by domain knowledge to...
For HTN planning, we formally characterize and classify four kinds of problem spaces in which each n...
We describe HTN-MAKER, an algorithm for learning hier-archical planning knowledge in the form of dec...
Translation-based approaches to planning allow for solving problems in complex and expressive formal...