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  • Planning techniques recorded a significant progress during recent years. However, many planning problems remain still hard even for modern planners. One of the most promising approaches is gathering additional knowledge by using learning techniques. Well known sort of knowledge - macro-operators, formalized like `normal` planning operators, represent a sequence of primitive planning operators. The other sort of knowledge consists of pruning unnecessary operators' instances (actions) by investigating connections (entanglements) between operators and initial or goal predicates. Advantageously, macro-operators and entanglements can be encoded directly in planning domains (or problems) and common planning systems can be applied on them. In this paper, we will show how we can put these approaches together. We will provide an experimental evaluation showing that combining these learning techniques can improve the planning process.
  • Planning techniques recorded a significant progress during recent years. However, many planning problems remain still hard even for modern planners. One of the most promising approaches is gathering additional knowledge by using learning techniques. Well known sort of knowledge - macro-operators, formalized like `normal` planning operators, represent a sequence of primitive planning operators. The other sort of knowledge consists of pruning unnecessary operators' instances (actions) by investigating connections (entanglements) between operators and initial or goal predicates. Advantageously, macro-operators and entanglements can be encoded directly in planning domains (or problems) and common planning systems can be applied on them. In this paper, we will show how we can put these approaches together. We will provide an experimental evaluation showing that combining these learning techniques can improve the planning process. (en)
Title
  • Combining Learning Techniques for Classical Planning: Macro operators and Entanglements
  • Combining Learning Techniques for Classical Planning: Macro operators and Entanglements (en)
skos:prefLabel
  • Combining Learning Techniques for Classical Planning: Macro operators and Entanglements
  • Combining Learning Techniques for Classical Planning: Macro operators and Entanglements (en)
skos:notation
  • RIV/68407700:21230/10:00173971!RIV11-GA0-21230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA201/08/0509)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
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http://linked.open...dnocenehoVysledku
  • 251229
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/10:00173971
http://linked.open...riv/jazykVysledku
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  • learning; planning; macro-operators; entanglements (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [9E78704F11CC]
http://linked.open...v/mistoKonaniAkce
  • Arras
http://linked.open...i/riv/mistoVydani
  • Cannes
http://linked.open...i/riv/nazevZdroje
  • Proceedings of The 22nd IEEE International Conference on Tools with Artificial Intelligence
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Chrpa, Lukáš
http://linked.open...vavai/riv/typAkce
http://linked.open...ain/vavai/riv/wos
  • 000287040000013
http://linked.open.../riv/zahajeniAkce
issn
  • 1082-3409
number of pages
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  • IEEE Computer Society
https://schema.org/isbn
  • 978-0-7695-4263-8
http://localhost/t...ganizacniJednotka
  • 21230
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