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Towards inducing hierarchical task network domain models for AI planning from examples

By Nona Elizabeth Richardson

Abstract

Domain modelling for AI Planning aims to form a database of facts about the ‘world’ being modelled.\ud This can be a complex process especially if there is a large number of objects or actions or both to be\ud modelled. This task can be facilitated by tools which induce operators or methods from examples.\ud Further, large and complex domains are more easily constructed if domain languages are used which\ud allow for hierarchical decomposition of domain components. Examples of such a decomposition are\ud class hierarchies and method hierarchies. This paper describes ongoing work which aims to\ud produce algorithms which learn effective hierarchical decompositions from examples

Topics: T1, QA75
Publisher: University of Huddersfield
Year: 2006
OAI identifier: oai:eprints.hud.ac.uk:3801

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Citations

  1. (2005). Action-relation Modelling System for Learning Acquisition Models.
  2. (2002). An Interactive Method for Inducing Operator Descriptions.
  3. (2006). Explanation-based Acquisition of Planning Operators.
  4. (2005). GIPO Graphical Interface for Planning with Objects.
  5. (2001). GIPO: An Integrated Graphical Tool to Support Knowledge Engineering in AI Planning.
  6. (2001). Learning Hierarchical Task Models by Defining and Refining Examples. doi

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