Research in classical planning has led to significant improvements in planner performance. However, lack of scalability with respect to the number of objects in the domain remains a longstanding open problem.
We invite contributions from researchers working to address this challenge. Possible research directions include generating plans that solve multiple problem instances by employing rich plan structures such as loops, using domain control knowledge for reducing the cost of planning, and other related areas. Common to many of these approaches is the notion of a generalized plan -- a rich representation that resembles a computer program with branches and loops. While approaches exploiting such representations have demonstrated promising results, many fundamental challenges remain.
The broad goal of this workshop is to provide a forum for discussion and evaluation of techniques for building scalable planners that utilize rich representations for expressing knowledge and solution plans. An additional objective is to re-evaluate some of the most fundamental, traditionally accepted notions in planning about plan structure and representation of domain knowledge. Some of the questions motivating this workshop are:
Topics of interest to this workshop bring together research being conducted in a range of areas, including classical planning, knowledge engineering, partial policies and hierarchical reinforcement learning, plan verification, and model checking:
Announcements9/18: Venue: Amphitheater 7, University of Macedonia.
8/3: Camera-ready deadline extended to Aug 20
8/3: List of Accepted Papers
6/23: Deadline extended to June 28
6/9: Updated information on the type of submissions
4/7: Invited Speaker: Paolo Traverso, IRST, Italy
4/7: Call for papers
19th International Conference on
Automated Planning and Scheduling