Learning Hierarchical Task Networks (HTNs)

This project aims at developing an unified architecture for learning and reusing Hierarchical Task Networks (HTNs): learning of Hierarchical Task Network (HTNs), Applying learned HTNs for problem-solving and an educational component where secondary-school students experience knowledge engineering activities (e.g., to encode HTNs).

Activities:

• Alexandra Coman defended her dissertation in the Spring 2013 and graduated in May 2013.

• Hector Munoz-Avila gave a Fall 2012 invited lectures on the topic of "HTN learning and HTN adaptation" at Huazhong University of Science and Technology, in Wuhan, China.

• Hector Munoz-Avila co-edited an special issue on Transfer of Structured Knowledge for the AI Magazine

• The source code and domains files of HTN-MAKER are now freely available under the GNU General Public License here!.

• Chad Hogg defended his dissertation in August 2011 and graduated in May 2012. Here is his Dissertation which is centered around HTN-MAKER

• Stephen Lee-Urban defended his dissertation in the Spring 2012 and graduated in May 2012.

• Hector Munoz-Avila gave an invited lecture on the topic of HTN learning at Universidad Carlos III de Madrid, in Madrid, Spain (2010).

• Hector Munoz-Avila gave an invited lecture on the topic of HTN learning at the Universidad Complutense de Madrid, in Madrid, Spain (2010)

• The IJCAI-09 Workshop on Learning Structural Knowledge From Observations Struck09, organized in part as a result of the disemination activities for this project.

• A summer 2007 Invited talk at National Taiwan University about HTNs, Learning and Computer games. Variants of this talk were given also at National Tsing Hua University and at SET Corporation. The latter took place in the summer 2008.

• A summer 2008 Invited talk at Hong Kong University of Science and Technology about our work on learning HTNs.

• We built a prototype of a Hierarchical Task Network Learner, called HTN-MAKER. HTN-Maker is an offline and incremental algorithm for learning the structural relations between tasks in a Hierarchical Task Network (HTN). HTN-MAKER receives as input a STRIPS domain model, a collection of STRIPS plans, and a collection of task definitions, and produces an HTN domain model. This work was presented at AAAI-08.
  • We are studying ways to augment partial HTNs with episodic knowledge (i.e., cases).

• We are studying ways to enhance Hierarchical Task Network Learners, all of which currently learn domains for deterministic domains, to non-deterministic domains. We developed techniques to make such an enhancement and used on HTN-Maker to obtain the first HTN learner in non-determinisitc domaisn, which we call ND-HTNMaker. This work was presented at IJCAI-09.

• We are currently enhancing experiments on RepairSHOP. RepairSHOP is a plan adaptation algorithm build on top of the HTN planner SHOP, which implements a variant of HTN planning called Ordered Task Decomposition. In this variant tasks are totally ordered and conditions are evaluated relative to the current state of the world, which is updated during planning.

• We are exploring using reinforcement learning techniques to learn HTNs in domains under uncertainty.

• We implemented an instructional game aimed K-12 students that provides a hands-on experience on aspects of building a knowledge base (e.g., HTN's methods).

• A course on Automated Planning developed under this project.

Publications:

Hankui Zhuoa, H., Munoz-Avila, H., Yang, Q. (2014) Learning Hierarchical Task Network Domains from Partially Observed Plan Traces. Artificial Intelligence Journal.
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Hogg, C., Munoz-Avila, H., Kuter, U. (2014) Learning Hierarchical Task Models from Input Traces. Computational Intelligence.
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Chad Hogg (2011) Learning Hiearchical Task Networks from traces and Semantically annotated Tasks.   PhD Dissertation. Computer Science and Engineering. Lehigh University.
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Chad Hogg, Ugur Kuter, and Hector Munoz-Avila (2010) Learning Methods to Generate Good Plans: Integrating HTN Learning and Reinforcement Learning.   To appear in Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-2010). AAAI Press.
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Chad Hogg, Ugur Kuter, and Hector Munoz-Avila (2009) Learning Hierarchical Task Networks for Nondeterministic Planning Domains.   To appear in Proceedings of the Twenty-first International Joint Conference on Artificial Intelligence (IJCAI-09). AAAI Press.
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Hankz Hankui Zhuo, Derek Hao Hu, Chad Hogg Qiang Yang, and Hector Munoz-Avila (2009) Learning HTN Method Preconditions and Action Models from Partial Observations.   To appear in Proceedings of the Twenty-first International Joint Conference on Artificial Intelligence (IJCAI-09). AAAI Press.
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Hankz Hankui Zhuo, Derek Hao Hu, Qiang Yang, Chad Hogg and Hector Munoz-Avila (2009) Learning Model Structures in AI Planning from Partial Observations.   To appear in Proceedings of the IJCAI-09 Workshop on Learning Structural Information from Traces (STRUCK-09). AAAI Press.
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Chad Hogg, Ugur Kuter, and Hector Munoz-Avila (2009) From Plan Traces to Hierarchical Task Networks Using Reinforcements: A Preliminary Report.   To appear in Proceedings of the IJCAI-09 Workshop on Learning Structural Information from Traces (STRUCK-09). AAAI Press.
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Hogg, Chad, Munoz-Avila, Hector, and Ugur Kuter. (2008) HTN-MAKER: Learning HTNs with Minimal Additional Knowledge Engineering Required.   To appear in Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI-08). AAAI Press.
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Hogg, C. & Munoz-Avila, H. (2007) Learning of Tasks Models for HTN Planning.   Proceedings of the ICAPS-07 Workshop on AI Planning and Learning (AIPL). AAAI Press.
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Lee-Urban, S., Parker, A., Kuter, U., Munoz-Avila, H., & Nau, D. (2007) Transfer Learning of Hierarchical Task-Network Planning Methods in a Real-Time Strategy Game.   Proceedings of the ICAPS-07 Workshop on ICAPS 2007 Workshop on Planning and Learning (AIPL). AAAI Press.
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Warfield, I., Hogg, C., Lee-Urban, S., Munoz-Avila, H. (2007) Adaptation of Hierarchical Task Network Plans.   Proceedings of the Twentieth Flairs International Conference (FLAIRS-07). AAAI Press.
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This material is based upon work supported by the National Science Foundation under Grant No. NSF 0642882. Any opinions, findings and conclusions or recomendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF)

Last updated: Tue. Aug. 27 11:49:51 EST 2013