Learning Generalized Plans Using Abstract Counting

Siddharth Srivastava, Neil Immerman, and Shlomo Zilberstein. Learning Generalized Plans Using Abstract Counting. Proceedings of the Twenty-Third Conference on Artificial Intelligence (AAAI), 991-997, Chicago, Illinois, 2008.


Given the complexity of planning, it is often beneficial to create plans that work for a wide class of problems. This facilitates reuse of existing plans for different instances drawn from the same problem or from an infinite family of similar problems. We define a class of such planning problems called generalized planning problems and present a novel approach for transforming classical plans into generalized plans. These algorithm-like plans include loops and work for problem instances having varying numbers of objects that must be manipulated to reach the goal. Our approach takes as input a classical plan for a certain problem instance. It outputs a generalized plan along with a classification of the problem instances where it is guaranteed to work. We illustrate the utility of our approach through results of a working implementation on various practical examples.

Bibtex entry:

  author	= {Siddharth Srivastava and Neil Immerman and Shlomo Zilberstein},
  title		= {Learning Generalized Plans Using Abstract Counting},
  booktitle     = {Proceedings of the Twenty-Third Conference on Artificial
  year		= {2008},
  pages		= {991-997},
  address       = {Chicago, Illinois},
  url		= {http://rbr.cs.umass.edu/shlomo/papers/SIZaaai08.html}

UMass Amherst