Neural network guided search control in partial order planning

Terry Zimmerman, Subbarao Kambhampati

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

2 Scopus citations

Abstract

Planning programs integrated with neural network (NN) were investigated to develop efficient search control methods in assisting planner's search for solution. Partial plan parameters that would likely evolve as a solution is entered to a planning program. The program was then modified to automatically produce vector for every partial plan used in its search process. The modified planner runs on a set of problem and generates two sets of input vector, one for network training and the other for testing. The training set of input vectors were used in designing and training a NN. The modified version of the partial order planner that incorporates the threshold function representing the successfully trained NN is developed to guide planners.

Original languageEnglish (US)
Title of host publicationInnovative Applications of Artificial Intelligence - Conference Proceedings
Editors Anon
Place of PublicationMenlo Park, CA, United States
PublisherAAAI
Pages1418
Number of pages1
StatePublished - 1996
EventProceedings of the 1996 8th Conference on Innovative Applications of Artificial Intelligence - Portland, OR, USA
Duration: Aug 4 1996Aug 8 1996

Other

OtherProceedings of the 1996 8th Conference on Innovative Applications of Artificial Intelligence
CityPortland, OR, USA
Period8/4/968/8/96

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ASJC Scopus subject areas

  • Software

Cite this

Zimmerman, T., & Kambhampati, S. (1996). Neural network guided search control in partial order planning. In Anon (Ed.), Innovative Applications of Artificial Intelligence - Conference Proceedings (pp. 1418). AAAI.