Learnable Representation for Real World Planning

Mihai Boicu, Gheorghe Tecuci, Bogdan Stanescu, Liviu Panait, and Cristina Cascaval

This paper presents a learnable representation for real-world planning systems. This representation is a significant extension of the ones used in the most recent systems from the Disciple family, the Disciple-Workaround system for plan generation, and the Disciple-COA system for plan critiquing. The representation is defined to support an integration of domain modeling, knowledge acquisition, learning and planning, in a mixed-initiative framework. It also helps to remove the current distinction between the development phase of a planning system and its maintenance phase. It provides an elegant solution to the knowledge expressiveness / knowledge efficiency trade-off, and allows reasoning with incomplete or partially incorrect knowledge. These qualities of the representation are supported by several experimental results.

This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.