Learning by analogical reasoning in general problem-solving
Learning by analogical reasoning in general problem-solving
Hybrid planning for partially hierarchical domains
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Combining Domain-Independent Planning and HTN Planning: The Duet Planner
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Planning through stochastic local search and temporal action graphs in LPG
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Translating HTNs to PDDL: a small amount of domain knowledge can go a long way
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
The LAMA planner: guiding cost-based anytime planning with landmarks
Journal of Artificial Intelligence Research
A hierarchical goal-based formalism and algorithm for single-agent planning
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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One drawback of Hierarchical Task Network (HTN) planning is the difficulty of 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, we define a simple formalism that extends classical planning to include problem decomposition using methods, and a planning algorithm based on this formalism. In our 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. Our planning algorithm, GoDeL (Goal Decomposition with 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, we 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.