Dr. Ugur Kuter collaborates on HTN planning paper for IJCAI-13

Dr. Ugur Kuter has collaborated with University of Maryland graduate students Ron Alford and Vikas Shivashankar and faculty Dr. Dana Nau on a paper that will appear in the proceedings of the 2013 International Joint Conference on Artificial Intelligence (IJCAI-13). This work analyzes Hierarchical Task Network (HTN) planning in light of its difficulty providing complete domain knowledge, i.e., a complete and correct set of HTN methods for every task. To provide a principled way to overcome this difficulty, the authors defined a simple formalism that extends classical planning to include problem decomposition using methods, and a planning algorithm based on this formalism.

Dr. Ugur Kuter has collaborated with University of Maryland graduate students Ron Alford and Vikas Shivashankar and faculty Dr. Dana Nau on a paper that will appear in the proceedings of the 2013 International Joint Conference on Artificial Intelligence (IJCAI-13). This work analyzes Hierarchical Task Network (HTN) planning in light of its difficulty providing complete domain knowledge, i.e., a complete and correct set of HTN methods for every task. To provide a principled way to overcome this difficulty, the authors defined a simple formalism that extends classical planning to include problem decomposition using methods, and a planning algorithm based on this formalism.

In this formalism, the methods specify ways to achieve goals (rather than tasks as in conventional HTN planning), and goals may be achieved even when no methods are available. The planning algorithm, GoDeL (Goal Decomposition using Landmarks), is sound and complete irrespective of whether the domain knowledge (i.e., the set of methods given to the planner) is complete. By comparing GoDeL's performance with varying amounts of domain knowledge across three benchmark planning domains, the authors show experimentally that (1) GoDeL works correctly with partial planning knowledge, (2) GoDeL's performance improves as more planning knowledge is given, and (3) when given full domain knowledge, GoDel matches the performance of a state-of-the-art hierarchical planner.