Learning Hierarchical Task Models from Input Traces
نویسندگان
چکیده
منابع مشابه
Learning Hierarchical Task Models from Input Traces
We describe HTN-Maker, an algorithm for learning hierarchical planning knowledge in the form of task-reduction methods for Hierarchical Task Networks (HTNs). HTN-Maker takes as input a set of planning states from a classical planning domain and plans that are applicable to those states, as well as a set of semantically-annotated tasks to be accomplished. The algorithm analyzes this semantic inf...
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We present HTN-MAKER, an offline and incremental algorithm for learning the structural relations between tasks in a Hierarchical Task Network (HTN). HTN-MAKER receives as input a STRIPS domain model, a collection of STRIPS plans, and a collection of task definitions, and produces an HTN domain model. HTN-MAKER is capable of learning an HTN domain model that reflects the provided task definition...
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Hierarchical Task Network (HTN) planning is an effective yet knowledge intensive problem-solving technique. It requires humans to encode knowledge in the form of methods and action models. Methods describe how to decompose tasks into subtasks and the preconditions under which those methods are applicable whereas action models describe how actions change the world. Encoding such knowledge is a d...
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Hierarchical Task Network planning is a fast and highly expressive formalism for problem solving, but systems based on this formalism depend on the existence of domain-specific knowledge constructs (methods) describing how and in what circumstances complex tasks may be reduced into simpler tasks in order to solve problems. Writing and debugging a set of methods for a new domain has in the past ...
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ژورنال
عنوان ژورنال: Computational Intelligence
سال: 2014
ISSN: 0824-7935,1467-8640
DOI: 10.1111/coin.12044