{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T17:30:15Z","timestamp":1780421415393,"version":"3.54.1"},"reference-count":44,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T00:00:00Z","timestamp":1744156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Deterministic hierarchical task network (HTN) planning assumes that planning evolves along a fully predictable path and neglects the quality of the plan in the partially observable environment. To bridge this research gap, this paper proposes an innovative probabilistic contingent HTN planner, named the High-Quality Contingent Planner (HQCP), designed to generate high-quality plans within partially observable contexts. Our methodology extends conventional HTN planning formalisms to accommodate for partial observability and assesses these extensions based on plan cost. Additionally, we propose a novel heuristic for high-quality plans and develop the integrated planning algorithm. These empirical studies verify the effectiveness and efficiency of the planner both in probabilistic contingent planning and in achieving plans of a high quality.<\/jats:p>","DOI":"10.3390\/a18040214","type":"journal-article","created":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T07:49:02Z","timestamp":1744184942000},"page":"214","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Probabilistic Contingent Planning Based on Hierarchical Task Network for High-Quality Plans"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-9388-7928","authenticated-orcid":false,"given":"Peng","family":"Zhao","sequence":"first","affiliation":[{"name":"Microsoft Corporation, Beijing 100080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Guanghua School of Management, Peking University, Beijing 100080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9208-1854","authenticated-orcid":false,"given":"Xuqi","family":"Su","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Shanghai Jiaotong University, Shanghai 200240, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Di","family":"Wu","sequence":"additional","affiliation":[{"name":"Guanghua School of Management, Peking University, Beijing 100080, China"},{"name":"School of Systems and Computing, University of New South Wales, Canberra 2612, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, Shanghai Jiaotong University, Shanghai 200240, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Business, University of New South Wales, Canberra 2612, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9402-1930","authenticated-orcid":false,"given":"Keqin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, AMA University, Quezon 1106, Philippines"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2482-8104","authenticated-orcid":false,"given":"Armando","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,9]]},"reference":[{"key":"ref_1","unstructured":"Xiao, Z., and Blanco, E. (2022, January 12\u201317). Are People Located in the Places They Mention in Their Tweets? A Multimodal Approach. Proceedings of the 29th International Conference on Computational Linguistics, Gyeongju, Republic of Korea."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xiao, Z., Huang, Y., and Blanco, E. (2023, January 1\u20134). Context Helps Determine Spatial Knowledge from Tweets. Proceedings of the Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings), Nusa Dua, Indonesia.","DOI":"10.18653\/v1\/2023.findings-ijcnlp.13"},{"key":"ref_3","first-page":"157","article-title":"An Adaptation Technique to Enhance HTN Planning","volume":"10","author":"Ahmad","year":"2023","journal-title":"IJCI Int. J. Comput. Inf."},{"key":"ref_4","unstructured":"Goldman, R.P., Zaidins, P., Kuter, U., and Nau, D. (2024, January 1\u20136). A Comparative Analysis of Plan Repair in HTN Planning. Proceedings of the 7th ICAPS Workshop on Hierarchical Planning, Banff, AB, Canada."},{"key":"ref_5","first-page":"655","article-title":"Review on Hierarchical Task Network Planning under Uncertainty","volume":"42","author":"Wang","year":"2016","journal-title":"Acta Autom. Sin."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hu, Y., and Zhuo, H.H. (2024, January 12\u201314). Multi-Task Reinforcement Learning with Cost-Based HTN Planning. Proceedings of the 2024 5th International Conference on Computer Engineering and Application (ICCEA), Hangzhou, China.","DOI":"10.1109\/ICCEA62105.2024.10603549"},{"key":"ref_7","unstructured":"Erol, K., Hendler, J., and Nau, D.S. (August, January 31). HTN Planning: Complexity and Expressivity. Proceedings of the AAAI, Seattle, WA, USA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/BF02136175","article-title":"Complexity Results for HTN Planning","volume":"18","author":"Erol","year":"1996","journal-title":"Ann. Math. Artif. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.knosys.2017.06.036","article-title":"Hierarchical Task Network Planning with Resources and Temporal Constraints","volume":"133","author":"Qi","year":"2017","journal-title":"Knowl.-Based Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.knosys.2016.08.029","article-title":"Hierarchical Task Network-Based Emergency Task Planning with Incomplete Information, Concurrency and Uncertain Duration","volume":"112","author":"Liu","year":"2016","journal-title":"Knowl.-Based Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3819","DOI":"10.3233\/JIFS-17681","article-title":"Resource-Constrained Hierarchical Task Network Planning under Uncontrollable Durations for Emergency Decision-Making","volume":"33","author":"Zhao","year":"2017","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"255","DOI":"10.3233\/JIFS-161557","article-title":"HTN Planning with Uncontrollable Durations for Emergency Decision-Making","volume":"33","author":"Zhao","year":"2017","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mai, Z., Zhang, J., Xu, Z., and Xiao, Z. (2024, January 2\u20134). Is LLaMA 3 Good at Sarcasm Detection? A Comprehensive Study. Proceedings of the 2024 7th International Conference on Machine Learning and Machine Intelligence (MLMI), Osaka, Japan.","DOI":"10.1145\/3696271.3696294"},{"key":"ref_14","unstructured":"H\u00f6ller, D., Behnke, G., Bercher, P., and Biundo, S. (2014, January 18\u201322). Language Classification of Hierarchical Planning Problems. Proceedings of the 21st European Conference on Artificial Intelligence, ECAI 2014, Prague, Czech Republic."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Sandamali, M., and Gamini, D. (2024). Comparison of HTN Planning and OR-Based Approaches for Solving Problems in Logistics Domains. Vidyodaya J. Sci., 27.","DOI":"10.31357\/vjs.v27i01.7495"},{"key":"ref_16","first-page":"22","article-title":"Task Allocation Planning Based on Hierarchical Task Network for National Economic Mobilization","volume":"5","author":"Zhao","year":"2024","journal-title":"J. Artif. Intell. Gen. Sci. (JAIGS)"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, J., Li, H., Ru, J., and Xu, H. (2024, January 25\u201327). Decision Support for Beyond Visual Range Air Combat Based on HTN Planning and Machine Learning. Proceedings of the 2024 36th Chinese Control and Decision Conference (CCDC), Xi\u2019an, China.","DOI":"10.1109\/CCDC62350.2024.10587348"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xiao, Z., Cui, Y., Mai, Z., Xu, Z., and Li, J. (2024). Corporate Event Prediction Using Earning Call Transcripts. Information Management and Big Data, Proceedings of the 10th Annual International Conference, SIMBig 2023, Mexico City, Mexico, 13\u201315 December 2023, Springer.","DOI":"10.1007\/978-3-031-63616-5_20"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xiao, Z., Mai, Z., Xu, Z., Cui, Y., and Li, J. (2023, January 25\u201326). Corporate Event Predictions Using Large Language Models. Proceedings of the 2023 10th International Conference on Soft Computing & Machine Intelligence (ISCMI), Mexico City, Mexico.","DOI":"10.1109\/ISCMI59957.2023.10458651"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"9917","DOI":"10.1007\/s00521-023-08228-2","article-title":"HTN Planning for Dynamic Vehicle Scheduling with Stochastic Trip Times","volume":"35","author":"Shen","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_21","unstructured":"Tang, Y., Meneguzzi, F., Sycara, K., and Parsons, S. (2011, January 7\u20138). Planning over MDPs through Probabilistic HTNs. Proceedings of the AAAI-11 Workshop on Generalized Planning, San Francisco, CA, USA."},{"key":"ref_22","unstructured":"Sohrabi, S., Baier, S., and McIlraith, S.A. (2009, January 11\u201317). HTN Planning with Preferences. Proceedings of the 21st International Joint Conference on Artificial Intelligence, Pasadena, CA, USA."},{"key":"ref_23","unstructured":"Kuter, U., Nau, D., Reisner, E., and Goldman, R. (2007, January 22\u201326). Conditionalization: Adapting Forward-Chaining Planners to Partially Observable Environments. Proceedings of the ICAPS 2007 Workshop on Planning and Execution for Real-World Systems, Providence, RI, USA."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1613\/jair.1141","article-title":"SHOP2: An HTN Planning System","volume":"20","author":"Nau","year":"2003","journal-title":"J. Artif. Intell. Res."},{"key":"ref_25","unstructured":"Bouguerra, A., and Karlsson, L. (2024, December 25). Hierarchical Task Planning Under Uncertainty. Available online: https:\/\/www.researchgate.net\/profile\/Lars-Karlsson-10\/publication\/2949969_Hierarchical_Task_Planning_under_Uncertainty\/links\/09e4150ffdb6d19c13000000\/Hierarchical-Task-Planning-under-Uncertainty.pdf."},{"key":"ref_26","first-page":"44","article-title":"PC-SHOP: A Probabilistic-Conditional Hierarchical Task Planner","volume":"2","author":"Bouguerra","year":"2005","journal-title":"Intell. Artif."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/S0004-3702(02)00379-X","article-title":"Contingent Planning under Uncertainty via Stochastic Satisfiability","volume":"147","author":"Majercik","year":"2003","journal-title":"Artif. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Brafman, R., and Shani, G. (2012, January 22\u201326). A Multi-Path Compilation Approach to Contingent Planning. Proceedings of the AAAI Conference on Artificial Intelligence, Toronto, ON, Canada.","DOI":"10.1609\/aaai.v26i1.8392"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Maliah, S., Brafman, R., Karpas, E., and Shani, G. (2014, January 21\u201326). Partially Observable Online Contingent Planning Using Landmark Heuristics. Proceedings of the International Conference on Automated Planning and Scheduling, Portsmouth, NH, USA.","DOI":"10.1609\/icaps.v24i1.13632"},{"key":"ref_30","unstructured":"Shmaryahu, D., Shani, G., Hoffmann, J., and Steinmetz, M. (2017, January 20). Partially Observable Contingent Planning for Penetration Testing. Proceedings of the Iwaise: First International Workshop on Artificial Intelligence in Security, Melbourne, Australia."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Shani, G. (2024, January 19\u201324). Heuristics for Partially Observable Stochastic Contingent Planning. Proceedings of the ECAI 2024, 27th European Conference on Artificial Intelligence, Santiago de Compostela, Spain.","DOI":"10.3233\/FAIA240983"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hogg, C., Kuter, U., and Munoz-Avila, H. (2010, January 11\u201315). Learning Methods to Generate Good Plans: Integrating Htn Learning and Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence, Atlanta, GA, USA.","DOI":"10.1609\/aaai.v24i1.7571"},{"key":"ref_33","unstructured":"Luo, J., Zhu, C., and Zhang, W. (2012). Messy Genetic Algorithm for the Optimum Solution Search of the HTN Planning. Foundations of Intelligent Systems, Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, ISKE2011, Shanghai, China, 15\u201317 December 2011, Springer."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Georgievski, I., and Lazovik, A. (2014, January 18\u201322). Utility-Based HTN Planning. Proceedings of the ECAI 2014, the Twenty-First European Conference on Artificial Intelligence, Prague, Czech Republic.","DOI":"10.3233\/978-1-61499-419-0-1013"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Behnke, G., H\u00f6ller, D., and Biundo, S. (2019, January 10\u201316). Finding Optimal Solutions in HTN Planning-A SAT-Based Approach. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), Macao, China.","DOI":"10.24963\/ijcai.2019\/764"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1613\/jair.1.11282","article-title":"HTN Planning as Heuristic Progression Search","volume":"67","author":"Bercher","year":"2020","journal-title":"J. Artif. Intell. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"107067","DOI":"10.1016\/j.knosys.2021.107067","article-title":"The Hierarchical Task Network Planning Method Based on Monte Carlo Tree Search","volume":"225","author":"Shao","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Behnke, G., and Speck, D. (2021, January 2\u20139). Symbolic Search for Optimal Total-Order HTN Planning. Proceedings of the AAAI Conference on Artificial Intelligence, Virtually.","DOI":"10.1609\/aaai.v35i13.17396"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yousefi, M., and Bercher, P. (2024, January 3\u20139). Laying the Foundations for Solving FOND HTN Problems: Grounding, Search, Heuristics (and Benchmark Problems). Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24, Jeju, Republic of Korea.","DOI":"10.24963\/ijcai.2024\/751"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chen, D., and Bercher, P. (2021, January 2\u201313). Fully Observable Nondeterministic HTN Planning\u2013Formalisation and Complexity Results. Proceedings of the International Conference on Automated Planning and Scheduling, Guangzhou, China.","DOI":"10.1609\/icaps.v31i1.15949"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chen, D.Z., and Bercher, P. (2022, January 13\u201324). Flexible Fond Htn Planning: A Complexity Analysis. Proceedings of the International Conference on Automated Planning and Scheduling, Singapore (Virtual).","DOI":"10.1609\/icaps.v32i1.19782"},{"key":"ref_42","unstructured":"Quemy, A., Schoenauer, M., and Dreo, J. (2023). MultiZenoTravel: A Tunable Benchmark for Multi-Objective Planning with Known Pareto Front. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Bercher, P., Behnke, G., H\u00f6ller, D., and Biundo, S. (2017, January 19\u201325). An Admissible HTN Planning Heuristic. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI, Melbourne, Australia.","DOI":"10.24963\/ijcai.2017\/68"},{"key":"ref_44","unstructured":"Weld, D.S., Anderson, C.R., and Smith, D.E. (1998, January 26\u201330). Extending Graphplan to Handle Uncertainty & Sensing Actions. Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98), Madison, WI, USA."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/4\/214\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:11:38Z","timestamp":1760029898000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/4\/214"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,9]]},"references-count":44,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["a18040214"],"URL":"https:\/\/doi.org\/10.3390\/a18040214","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,9]]}}}