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.


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