Model-lite planning for the Web age masses: The challenges of planning with incomplete and evolving domain models

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Scopus citations

Abstract

The automated planning community has traditionally focused on the efficient synthesis of plans given a complete domain theory. In the past several years, this line of work met with significant successes, and the future course of the community seems to be set on efficient planning with even richer models. While this line of research has its applications, there are also many domains and scenarios where the first bottleneck is getting the domain model at any level of completeness. In these scenarios, the modeling burden automatically renders the planning technology unusable. To counter this, I will motivate model-lite planning technology aimed at reducing the domain-modeling burden (possibly at the expense of reduced functionality), and outline the research challenges that need to be addressed to realize it.

Original languageEnglish (US)
Title of host publicationAAAI-07/IAAI-07 Proceedings
Subtitle of host publication22nd AAAI Conference on Artificial Intelligence and the 19th Innovative Applications of Artificial Intelligence Conference
Pages1601-1604
Number of pages4
StatePublished - Nov 28 2007
EventAAAI-07/IAAI-07 Proceedings: 22nd AAAI Conference on Artificial Intelligence and the 19th Innovative Applications of Artificial Intelligence Conference - Vancouver, BC, Canada
Duration: Jul 22 2007Jul 26 2007

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume2

Other

OtherAAAI-07/IAAI-07 Proceedings: 22nd AAAI Conference on Artificial Intelligence and the 19th Innovative Applications of Artificial Intelligence Conference
CountryCanada
CityVancouver, BC
Period7/22/077/26/07

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ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Kambhampati, S. (2007). Model-lite planning for the Web age masses: The challenges of planning with incomplete and evolving domain models. In AAAI-07/IAAI-07 Proceedings: 22nd AAAI Conference on Artificial Intelligence and the 19th Innovative Applications of Artificial Intelligence Conference (pp. 1601-1604). (Proceedings of the National Conference on Artificial Intelligence; Vol. 2).