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\n\n \n \n \n \n \n \n Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021).\n \n \n \n \n\n\n \n Pascal Bercher; Jane Jean Kiam; Zhanhao Xiao; and Ron Alford.,\n editors.\n \n\n\n \n\n\n\n 2021.\n
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@Proceedings{HPlan2021proceedings,\n editor = {Pascal Bercher and Jane Jean Kiam and Zhanhao Xiao and Ron Alford},\n title = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n year = {2021},\n url_website = {http://hplan2021.hierarchical-task.net},\n url_proceedings = {https://hierarchical-task.net/publications/hplan/HPlanProceedings-2021.pdf}\n}\n\n
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\n\n \n \n \n \n \n \n A Hierarchical Approach to Multi-Agent Path Finding.\n \n \n \n \n\n\n \n Han Zhang; Mingze Yao; Ziang Liu; Jiaoyang Li; Lucas Terr; Shao-Hung Chan; T. K. Satish Kumar; and Sven Koenig.\n\n\n \n\n\n\n In
Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), pages 1–7, 2021. \n
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@InProceedings{HPlan2021paper1,\n author = {Han Zhang and Mingze Yao and Ziang Liu and Jiaoyang Li and Lucas Terr and Shao-Hung Chan and T. K. Satish Kumar and Sven Koenig},\n title = {A Hierarchical Approach to Multi-Agent Path Finding},\n booktitle = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n year = {2021},\n pages = {1--7},\n abstract = {The Multi-Agent Path Finding (MAPF) problem arises in many real-world applications, ranging from automated warehousing to multi-drone delivery. Solving the MAPF problem optimally is NP-hard, and existing optimal and bounded-suboptimal MAPF solvers thus usually do not scale to large MAPF instances. Greedy MAPF solvers scale to large MAPF instances, but their solution qualities are often bad. In this paper, we therefore propose a novel MAPF solver, Hierarchical Multi-Agent Path Planner (HMAPP), which creates a spatial hierarchy by partitioning the environment into multiple regions and decomposes a MAPF instance into smaller MAPF sub-instances for each region. For each sub-instance, it uses a bounded-suboptimal MAPF solver to solve it with good solution quality. Our experimental results show that HMAPP solves as large MAPF instances as greedy MAPF solvers while achieving better solution qualities on various maps.},\n url_paper = {https://hierarchical-task.net/publications/hplan/2021/HPlan2021-paper1.pdf},\n url_presentation = {https://www.youtube.com/watch?v=eBZALSeZpXU&t=3s}\n}\n\n
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\n The Multi-Agent Path Finding (MAPF) problem arises in many real-world applications, ranging from automated warehousing to multi-drone delivery. Solving the MAPF problem optimally is NP-hard, and existing optimal and bounded-suboptimal MAPF solvers thus usually do not scale to large MAPF instances. Greedy MAPF solvers scale to large MAPF instances, but their solution qualities are often bad. In this paper, we therefore propose a novel MAPF solver, Hierarchical Multi-Agent Path Planner (HMAPP), which creates a spatial hierarchy by partitioning the environment into multiple regions and decomposes a MAPF instance into smaller MAPF sub-instances for each region. For each sub-instance, it uses a bounded-suboptimal MAPF solver to solve it with good solution quality. Our experimental results show that HMAPP solves as large MAPF instances as greedy MAPF solvers while achieving better solution qualities on various maps.\n
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\n\n \n \n \n \n \n \n Compiling HTN Plan Verification Problems into HTN Planning Problems.\n \n \n \n \n\n\n \n Daniel Höller; Julia Wichlacz; Pascal Bercher; and Gregor Behnke.\n\n\n \n\n\n\n In
Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), pages 8–15, 2021. \n
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@InProceedings{HPlan2021paper2,\n author = {Daniel H\\"{o}ller and Julia Wichlacz and Pascal Bercher and Gregor Behnke},\n title = {Compiling HTN Plan Verification Problems into HTN Planning Problems},\n booktitle = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n year = {2021},\n pages = {8--15},\n abstract = {Plan Verification is the task of deciding whether a sequence of actions is a solution for a given planning problem. In HTN planning, the task is computationally expensive and may be up to NP-hard. However, there are situations where it needs to be solved, e.g. when a solution is post-processed, in systems using approximation, or just to validate whether a planning system works correctly (e.g. for debugging or in a competition). In the literature, there are verification systems based on translations to propositional logic and based on techniques from parsing. Here we present a third approach and translate HTN plan verification problems into HTN planning problems. These can be solved using any HTN planning system. We test our solver on the set of solutions from the 2020 International Planning Competition. Our evaluation is yet preliminary, because it does not include all systems from the literature, but it already shows that our approach performs well compared with the included systems.},\n url_paper = {https://hierarchical-task.net/publications/hplan/2021/HPlan2021-paper2.pdf},\n url_presentation = {https://www.youtube.com/watch?v=SkVUtQJlVZc}\n}\n\n
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\n Plan Verification is the task of deciding whether a sequence of actions is a solution for a given planning problem. In HTN planning, the task is computationally expensive and may be up to NP-hard. However, there are situations where it needs to be solved, e.g. when a solution is post-processed, in systems using approximation, or just to validate whether a planning system works correctly (e.g. for debugging or in a competition). In the literature, there are verification systems based on translations to propositional logic and based on techniques from parsing. Here we present a third approach and translate HTN plan verification problems into HTN planning problems. These can be solved using any HTN planning system. We test our solver on the set of solutions from the 2020 International Planning Competition. Our evaluation is yet preliminary, because it does not include all systems from the literature, but it already shows that our approach performs well compared with the included systems.\n
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\n\n \n \n \n \n \n \n Domain Analysis: A Preprocessing Method that Reduces the Size of the Search Tree in Hybrid Planning.\n \n \n \n \n\n\n \n Michael Staud.\n\n\n \n\n\n\n In
Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), pages 16–20, 2021. \n
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@InProceedings{HPlan2021paper3,\n author = {Michael Staud},\n title = {Domain Analysis: A Preprocessing Method that Reduces the Size of the Search Tree in Hybrid Planning},\n booktitle = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n year = {2021},\n pages = {16--20},\n abstract = {We introduce a new method that can reduce the size of the search tree in hybrid planning. Hybrid planning fuses task insertion HTN planning with POCL planning. As planning is a computationally difficult problem, the size of the search tree can grow exponentially to the size of the problem.<br/>\n We create so-called plan templates in a preprocessing step of the domain. They can be used to replace an abstract task in a partial plan. This task then no longer needs to be decomposed.<br/>\n We provide empirical evidence in favor of this approach and show that the use of plan templates can drastically reduce the size of the search tree in hybrid planners. We use a PANDA-like planner as a testbed and publicly available planning domains to verify our claims.},\n url_paper = {https://hierarchical-task.net/publications/hplan/2021/HPlan2021-paper3.pdf},\n url_presentation = {https://www.youtube.com/watch?v=u3UE3-txp1o}\n}\n\n
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\n We introduce a new method that can reduce the size of the search tree in hybrid planning. Hybrid planning fuses task insertion HTN planning with POCL planning. As planning is a computationally difficult problem, the size of the search tree can grow exponentially to the size of the problem.
We create so-called plan templates in a preprocessing step of the domain. They can be used to replace an abstract task in a partial plan. This task then no longer needs to be decomposed.
We provide empirical evidence in favor of this approach and show that the use of plan templates can drastically reduce the size of the search tree in hybrid planners. We use a PANDA-like planner as a testbed and publicly available planning domains to verify our claims.\n
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\n\n \n \n \n \n \n \n GTPyhop: A Hierarchical Goal+Task Planner Implemented in Python.\n \n \n \n \n\n\n \n Dana Nau; Yash Bansod; Sunandita Patra; Mark Roberts; and Ruoxi Li.\n\n\n \n\n\n\n In
Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), pages 21–25, 2021. \n
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@InProceedings{HPlan2021paper4,\n author = {Dana Nau and Yash Bansod and Sunandita Patra and Mark Roberts and Ruoxi Li},\n title = {GTPyhop: A Hierarchical Goal+Task Planner Implemented in Python},\n booktitle = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n year = {2021},\n pages = {21--25},\n abstract = {The Pyhop planner, released in 2013, was a simple SHOP-style planner written in Python. It was designed to be easily usable as an embedded system in conventional applications such as game programs. Although little effort was made to publicize Pyhop, its simplicity, ease of use, and understandability led to its use in a number of projects beyond its original intent, and to publications by others.<br/>\n GTPyhop (Goal-and-Task Pyhop) is an extended version of Pyhop that can plan for both goals and tasks, using a combination of SHOP-style task decomposition and GDP-style goal decomposition. It provides a totally-ordered version of Goal-Task-Network (GTN) planning without sharing and task insertion. GTPyhop’s ability to represent and reason about both goals and tasks provides a high degree of flexibility for representing objectives in whichever form seems more natural to the domain designer.},\n url_paper = {https://hierarchical-task.net/publications/hplan/2021/HPlan2021-paper4.pdf},\n url_presentation = {https://www.youtube.com/watch?v=O_xmFvPd1wE}\n}\n\n
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\n The Pyhop planner, released in 2013, was a simple SHOP-style planner written in Python. It was designed to be easily usable as an embedded system in conventional applications such as game programs. Although little effort was made to publicize Pyhop, its simplicity, ease of use, and understandability led to its use in a number of projects beyond its original intent, and to publications by others.
GTPyhop (Goal-and-Task Pyhop) is an extended version of Pyhop that can plan for both goals and tasks, using a combination of SHOP-style task decomposition and GDP-style goal decomposition. It provides a totally-ordered version of Goal-Task-Network (GTN) planning without sharing and task insertion. GTPyhop’s ability to represent and reason about both goals and tasks provides a high degree of flexibility for representing objectives in whichever form seems more natural to the domain designer.\n
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\n\n \n \n \n \n \n \n Integrating Planning and Acting With a Re-Entrant HTN Planner.\n \n \n \n \n\n\n \n Yash Bansod; Dana Nau; Sunandita Patra; and Mak Roberts.\n\n\n \n\n\n\n In
Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), pages 26–34, 2021. \n
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@InProceedings{HPlan2021paper5,\n author = {Yash Bansod and Dana Nau and Sunandita Patra and Mak Roberts},\n title = {Integrating Planning and Acting With a Re-Entrant HTN Planner},\n booktitle = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n year = {2021},\n pages = {26--34},\n abstract = {A major problem with integrating HTN planning and acting is that, unless the HTN methods are very carefully written, unexpected problems can occur when attempting to replan if execution errors or other unexpected conditions occur during acting. To overcome this problem, we present a re-entrant HTN planning algorithm that can be restarted for replanning purposes at the point where an execution error occurred, and an HTN acting algorithm that can restart the HTN planner at this point. We show through experiments that our algorithm is an improvement over a widely used approach to planning and control.},\n url_paper = {https://hierarchical-task.net/publications/hplan/2021/HPlan2021-paper5.pdf},\n url_presentation = {https://www.youtube.com/watch?v=pT8WJXoR_Jo}\n}\n\n\n
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\n A major problem with integrating HTN planning and acting is that, unless the HTN methods are very carefully written, unexpected problems can occur when attempting to replan if execution errors or other unexpected conditions occur during acting. To overcome this problem, we present a re-entrant HTN planning algorithm that can be restarted for replanning purposes at the point where an execution error occurred, and an HTN acting algorithm that can restart the HTN planner at this point. We show through experiments that our algorithm is an improvement over a widely used approach to planning and control.\n
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\n\n \n \n \n \n \n \n On the Computational Complexity of Correcting HTN Domain Models.\n \n \n \n \n\n\n \n Songtuan Lin; and Pascal Bercher.\n\n\n \n\n\n\n In
Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), pages 35–43, 2021. \n
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@InProceedings{HPlan2021paper6,\n author = {Songtuan Lin and Pascal Bercher},\n title = {On the Computational Complexity of Correcting HTN Domain Models},\n booktitle = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n year = {2021},\n pages = {35--43},\n abstract = {Incorporating user requests into planning processes is a key concept in developing flexible planning technologies. Such systems may be required to change its planning model to adapt to certain user requests. In this paper, we assume a user provides a non-solution plan to a system and asks it to change the planning model so that the plan becomes a solution. We study the computational complexity of deciding whether such changes exist in the context of Hierarchical Task Network (HTN) planning. We prove that the problem is NP-complete in general independent of what or how many changes are allowed. We also identify several conditions which make the problem tractable when they are satisfied.},\n url_paper = {https://hierarchical-task.net/publications/hplan/2021/HPlan2021-paper6.pdf},\n url_presentation = {https://www.youtube.com/watch?v=05DE4U6_Qh8}\n}\n\n\n
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\n Incorporating user requests into planning processes is a key concept in developing flexible planning technologies. Such systems may be required to change its planning model to adapt to certain user requests. In this paper, we assume a user provides a non-solution plan to a system and asks it to change the planning model so that the plan becomes a solution. We study the computational complexity of deciding whether such changes exist in the context of Hierarchical Task Network (HTN) planning. We prove that the problem is NP-complete in general independent of what or how many changes are allowed. We also identify several conditions which make the problem tractable when they are satisfied.\n
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\n\n \n \n \n \n \n \n On the Verification of Totally-Ordered HTN Plans.\n \n \n \n \n\n\n \n Roman Barták; Simona Ondrčková; Gregor Behnke; and Pascal Bercher.\n\n\n \n\n\n\n In
Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), pages 44–48, 2021. \n
A follow-up paper was later accepted at ICTAI 2021.\n\n
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@InProceedings{HPlan2021paper7,\n author = {Roman Bart{\\'a}k and Simona Ondr\\v{c}kov\\'{a} and Gregor Behnke and Pascal Bercher},\n title = {On the Verification of Totally-Ordered HTN Plans},\n booktitle = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n year = {2021},\n pages = {44--48},\n abstract = {Verifying HTN plans is an intractable problem with two existing approaches to solve the problem. One technique is based on compilation to SAT. Another method is using parsing, and it is currently the fastest technique for verifying HTN plans. In this paper, we propose an extension of the parsing-based approach to verify totally-ordered HTN plans more efficiently. This problem is known to be tractable if no state constraints are included, and we show theoretically and empirically that the modified parsing approach achieves better performance than the currently fastest HTN plan verifier when applied to totally-ordered HTN plans.},\n url_paper = {https://hierarchical-task.net/publications/hplan/2021/HPlan2021-paper7.pdf},\n note = {A follow-up paper was later accepted at ICTAI 2021.},\n url_presentation = {https://www.youtube.com/watch?v=zqUOjWru-TE}\n}\n\n
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\n Verifying HTN plans is an intractable problem with two existing approaches to solve the problem. One technique is based on compilation to SAT. Another method is using parsing, and it is currently the fastest technique for verifying HTN plans. In this paper, we propose an extension of the parsing-based approach to verify totally-ordered HTN plans more efficiently. This problem is known to be tractable if no state constraints are included, and we show theoretically and empirically that the modified parsing approach achieves better performance than the currently fastest HTN plan verifier when applied to totally-ordered HTN plans.\n
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\n\n \n \n \n \n \n \n Solving Hierarchical Auctions with HTN Planning.\n \n \n \n \n\n\n \n Antoine Milot; Estelle Chauveau; Simon Lacroix; and Charles Lesire.\n\n\n \n\n\n\n In
Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), pages 49–56, 2021. \n
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@InProceedings{HPlan2021paper8,\n author = {Antoine Milot and Estelle Chauveau and Simon Lacroix and Charles Lesire},\n title = {Solving Hierarchical Auctions with HTN Planning},\n booktitle = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n year = {2021},\n pages = {49--56},\n abstract = {This paper presents a preliminary approach to solve the Multi-Robot Task Allocation problem through hierarchical auctions combined with the use of HTN planning. We present the global approach and the challenges arisen by partially-ordered HTNs through some examples. We finally outline some options to integrate such constraints in the allocation scheme.},\n url_paper = {https://hierarchical-task.net/publications/hplan/2021/HPlan2021-paper8.pdf}\n}\n\n
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\n This paper presents a preliminary approach to solve the Multi-Robot Task Allocation problem through hierarchical auctions combined with the use of HTN planning. We present the global approach and the challenges arisen by partially-ordered HTNs through some examples. We finally outline some options to integrate such constraints in the allocation scheme.\n
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\n\n \n \n \n \n \n \n Solving POMDPs online through HTN Planning and Monte Carlo Tree Search.\n \n \n \n \n\n\n \n Robert P. Goldman.\n\n\n \n\n\n\n In
Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), pages 57–61, 2021. \n
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@InProceedings{HPlan2021paper9,\n author = {Robert P. Goldman},\n title = {Solving POMDPs online through HTN Planning and Monte Carlo Tree Search},\n booktitle = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n year = {2021},\n pages = {57--61},\n abstract = {This paper describes our SHOPPINGS PREE HTN algorithm for online planning in Partially Observable Markov Decision Processes (POMDPs). SHOPPING S PREE combines the HTN planning algorithm from S HOP 3, extensions to SHOP3’s representation to handle partial observability, and Monte Carlo Tree Search for efficient sampling in the problem space. This paper presents only the algorithm and initial notes on the implementation: this is work in progress.},\n url_paper = {https://hierarchical-task.net/publications/hplan/2021/HPlan2021-paper9.pdf},\n url_presentation = {https://www.youtube.com/watch?v=YMls2DbkS_A}\n}\n\n
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\n This paper describes our SHOPPINGS PREE HTN algorithm for online planning in Partially Observable Markov Decision Processes (POMDPs). SHOPPING S PREE combines the HTN planning algorithm from S HOP 3, extensions to SHOP3’s representation to handle partial observability, and Monte Carlo Tree Search for efficient sampling in the problem space. This paper presents only the algorithm and initial notes on the implementation: this is work in progress.\n
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\n\n \n \n \n \n \n \n Task and Situation Structures for Service Agent Planning.\n \n \n \n \n\n\n \n Hao Yang; Tavan Eftekhar; Chad Esselink; Yan Ding; ; and Shiqi Zhang.\n\n\n \n\n\n\n In
Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), 2021. \n
This is an extended version of a paper accepted at ICCBR 2021.\n\n
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@InProceedings{HPlan2021paper10,\n author = {Hao Yang and Tavan Eftekhar and Chad Esselink and Yan Ding and and Shiqi Zhang},\n title = {Task and Situation Structures for Service Agent Planning},\n booktitle = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n year = {2021},\n abstract = {Everyday tasks are characterized by their varieties and variations, and frequently are not clearly specified to service agents. This paper presents a comprehensive approach to enable a service agent to deal with everyday tasks in open, uncontrolled environments. We introduce a generic structure for representing tasks, and another structure for representing situations. Based on the two newly introduced structures, we present a methodology of situation handling that avoids hardcoding domain rules while improving the scalability of real-world task planning systems.},\n url_paper = {https://arxiv.org/pdf/2107.12851.pdf},\n note = {This is an extended version of a paper accepted at ICCBR 2021.}\n}\n\n
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\n Everyday tasks are characterized by their varieties and variations, and frequently are not clearly specified to service agents. This paper presents a comprehensive approach to enable a service agent to deal with everyday tasks in open, uncontrolled environments. We introduce a generic structure for representing tasks, and another structure for representing situations. Based on the two newly introduced structures, we present a methodology of situation handling that avoids hardcoding domain rules while improving the scalability of real-world task planning systems.\n
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\n\n \n \n \n \n \n \n The Complexity of Flexible FOND HTN Planning.\n \n \n \n \n\n\n \n Dillon Chen; and Pascal Bercher.\n\n\n \n\n\n\n In
Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), pages 62–70, 2021. \n
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@InProceedings{HPlan2021paper11,\n author = {Dillon Chen and Pascal Bercher},\n title = {The Complexity of Flexible FOND HTN Planning},\n booktitle = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n year = {2021},\n pages = {62--70},\n abstract = {Hierarchical Task Network (HTN) planning is an expressive planning formalism that has often been advocated as a first choice to address real-world problems. Yet only a few extensions exist that can deal with the many challenges encountered in the real world. One of them is the capability to express uncertainty. Recently, a new HTN formalism for Fully Observable Nondeterministic (FOND) problems was proposed and studied theoretically. In this paper, we lay out limitations of that formalism and propose an alternative definition, which addresses and resolves such limitations. We conduct a complexity study of an alternative, more flexible formalism and provide tight complexity bounds for most of the investigated special cases of the problem.},\n url_paper = {https://hierarchical-task.net/publications/hplan/2021/HPlan2021-paper11.pdf},\n url_presentation = {https://www.youtube.com/watch?v=RtmJ0djVi-8}\n}\n\n
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\n Hierarchical Task Network (HTN) planning is an expressive planning formalism that has often been advocated as a first choice to address real-world problems. Yet only a few extensions exist that can deal with the many challenges encountered in the real world. One of them is the capability to express uncertainty. Recently, a new HTN formalism for Fully Observable Nondeterministic (FOND) problems was proposed and studied theoretically. In this paper, we lay out limitations of that formalism and propose an alternative definition, which addresses and resolves such limitations. We conduct a complexity study of an alternative, more flexible formalism and provide tight complexity bounds for most of the investigated special cases of the problem.\n
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\n\n \n \n \n \n \n \n Temporal Hierarchical Task Network Planning with Nested Multi-Vehicle Routing Problems – A Challenge to be Resolved.\n \n \n \n \n\n\n \n Jane Jean Kiam; Pascal Bercher; and Axel Schulte.\n\n\n \n\n\n\n In
Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), pages 71–75, 2021. \n
This is a challenge paper.\n\n
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@InProceedings{HPlan2021paper12,\n author = {Jane Jean Kiam and Pascal Bercher and Axel Schulte},\n title = {Temporal Hierarchical Task Network Planning with Nested Multi-Vehicle Routing Problems -- A Challenge to be Resolved},\n booktitle = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n year = {2021},\n pages = {71--75},\n note = {This is a challenge paper.},\n abstract = {This paper focuses on presenting a complex real-world planning application based on a rescue mission. While temporal hierarchical planning seems to be a promising solution to such a class of problems, given its ability to consider experts' knowledge and dissect the search space, many major challenges of complex real-world planning problems are not addressed yet formally, i.e. recursive decomposition to achieve a goal state, optimization of utility functions defined for abstract tasks, and optimal allocation of tasks to multiple actors.},\n url_paper = {https://hierarchical-task.net/publications/hplan/2021/HPlan2021-paper12.pdf},\n url_presentation = {https://www.youtube.com/watch?v=R9JUXsd3kC4}\n}\n\n
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\n This paper focuses on presenting a complex real-world planning application based on a rescue mission. While temporal hierarchical planning seems to be a promising solution to such a class of problems, given its ability to consider experts' knowledge and dissect the search space, many major challenges of complex real-world planning problems are not addressed yet formally, i.e. recursive decomposition to achieve a goal state, optimization of utility functions defined for abstract tasks, and optimal allocation of tasks to multiple actors.\n
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\n\n \n \n \n \n \n \n Towards Robust Constraint Satisfaction in Hybrid Hierarchical Planning.\n \n \n \n \n\n\n \n Tobias Schwartz; Michael Sioutis; and Diedrich Wolter.\n\n\n \n\n\n\n In
Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021), pages 76–80, 2021. \n
This is a challenge paper.\n\n
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@InProceedings{HPlan2021paper13,\n author = {Tobias Schwartz and Michael Sioutis and Diedrich Wolter},\n title = {Towards Robust Constraint Satisfaction in Hybrid Hierarchical Planning},\n booktitle = {Proceedings of the 4th ICAPS Workshop on Hierarchical Planning (HPlan 2021)},\n year = {2021},\n pages = {76--80},\n note = {This is a challenge paper.},\n abstract = {Hybrid planning is essential for real world applications, as it allows for reasoning with different forms of abstract knowledge, such as time, space or resources. This unavoidably leads to a combinatorial explosion of the search space that has previously been tackled using a hierarchical task network (HTN) planning approach. Existing HTN planners mostly focus on finding a solution as fast as possible, with only recent work considering length-optimal solutions. In real world scenarios, it can easily happen that the environment changes before a plan is fully executed. We are motivated to conduct planning in such a way that the solution has the best chance of withstanding such changes in the environment. We call this ability the robustness of a solution. Defining robustness, however, is an inherently difficult challenge, as many different forms and notions exist. In this paper, we start the discussion by outlining a possible notion of robustness recently introduced in the light of Qualitative Spatial Reasoning (QSR) within the scope of hybrid hierarchical planning.},\n url_paper = {https://hierarchical-task.net/publications/hplan/2021/HPlan2021-paper13.pdf},\n url_presentation = {https://www.youtube.com/watch?v=yTPprq1vgYs}\n}\n\n\n\n\n\n\n\n
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\n Hybrid planning is essential for real world applications, as it allows for reasoning with different forms of abstract knowledge, such as time, space or resources. This unavoidably leads to a combinatorial explosion of the search space that has previously been tackled using a hierarchical task network (HTN) planning approach. Existing HTN planners mostly focus on finding a solution as fast as possible, with only recent work considering length-optimal solutions. In real world scenarios, it can easily happen that the environment changes before a plan is fully executed. We are motivated to conduct planning in such a way that the solution has the best chance of withstanding such changes in the environment. We call this ability the robustness of a solution. Defining robustness, however, is an inherently difficult challenge, as many different forms and notions exist. In this paper, we start the discussion by outlining a possible notion of robustness recently introduced in the light of Qualitative Spatial Reasoning (QSR) within the scope of hybrid hierarchical planning.\n
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