Abstract

Planning problems are naturally knowledge rich. Yet, most planning research to date has focused on languages and algorithms to represent and search through the decision space in order to generate a feasible solution. This paper surveys previous work on extending planning algorithms with expressive representations of knowledge, in particular description logics, to reasoning about action, plan, and goal taxonomies. These approaches should be of interest to planning practitioners by also to Semantic Web researchers who are currently developing web service languages based on planning languages and in combination with description logics.

Date

December 14, 2025

Authors

Yolanda Gil

Source

Course literature in CS541-Artificial Intelligence Planning