2011/11/30 by Georgievski, Ilče, Alexander Lazovik, Marco Aiello +2 · 1 citation
Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Constraint Satisfaction and Optimization #Distributed #FOS: Computer and information sciences #Parallel #Semantic Web and Ontologies #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.1111.7025
openalex publication_date 2011/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Hierarchical Task Network (HTN) planning uses task decomposition to plan for an executable sequence of actions as a solution to a problem. In order to reason effectively, an HTN planner needs expressive domain knowledge. For instance, a simplified HTN planning system such as JSHOP2 uses such expressivity and avoids some task interactions due to the increased complexity of the planning process. We address the possibility of simplifying the domain representation needed for an HTN planner to find good solutions, especially in real-world domains describing home and building automation environments. We extend the JSHOP2 planner to reason about task interaction that happens when task's effects are already achieved by other tasks. The planner then prunes some of the redundant searches that can occur due to the planning process's interleaving nature. We evaluate the original and our improved planner on two benchmark domains. We show that our planner behaves better by using simplified domain knowledge and outperforms JSHOP2 in a number of relevant cases.