Adaptive behavioral programming

Nir Eitan, David Harel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We introduce a way to program adaptive reactive systems, using behavioral, scenario-based programming. Extending the semantics of live sequence charts with reinforcements allows the programmer not only to specify what the system should do or must not do, but also what it should try to do, in an intuitive and incremental way. By integrating scenario-based programs with reinforcement learning methods, the program can adapt to the environment, and try to achieve the desired goals. Visualization methods and modular learning decompositions, based on the unique structure of the program, are suggested, and result in an efficient development process and a fast learning rate.

Original languageEnglish
Title of host publicationProceedings - 2011 23rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2011
Pages685-692
Number of pages8
DOIs
StatePublished - 2011
Event23rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2011 - Boca Raton, FL, United States
Duration: 7 Nov 20119 Nov 2011

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI

Conference

Conference23rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2011
Country/TerritoryUnited States
CityBoca Raton, FL
Period7/11/119/11/11

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Computer Science Applications

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