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Recent publications (last 12 months):

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• Roman Barták, Simona Ondrčková, Gregor Behnke and Pascal Bercher.
  Correcting Hierarchical Plans by Action Deletion.
  In Proceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning (KR-2021). 2021.
  (PDF)
  

• Roman Barták, Simona Ondrčková, Gregor Behnke and Pascal Bercher.
  Correcting Hierarchical Plans by Action Deletion.
  In Proceedings of the Fourth ICAPS Workshop on Hierarchical Planning. 2021.
  (PDF)
  

• Roman Barták, Simona Ondrčková, Gregor Behnke and Pascal Bercher.
  On the Verification of Totally-Ordered HTN Plans.
  In Proceedings of the Fourth ICAPS Workshop on Hierarchical Planning, pp. 44-48. 2021.
  (PDF)
  

• Daniel Höller, Julia Whichlacz, Pascal Bercher and Gregor Behnke.
  Compiling HTN Plan Verification Problems into HTN Planning Problems.
  In Proceedings of the Fourth ICAPS Workshop on Hierarchical Planning, pp. 8-15. 2021.
  (PDF)
  

• Pascal Bercher, Gregor Behnke, Matthias Kraus, Marvin Schiller, Dietrich Manstetten, Michael Dambier, Michael Dorna, Wolfgang Minker, Birte Glimm and Susanne Biundo.
  Do It Yourself, but Not Alone: Companion-Technology for Home Improvement - Bringing a Planning-Based Interactive DIY Assistant to Life.
  Künstliche Intelligenz -- Special Issue on NLP and Semantics. 2021.
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  We report on the technology transfer project "Do it yourself, but not alone: Companion-Technology for Home Improvement" that was carried out by Ulm University in cooperation with Robert Bosch GmbH. We developed a prototypical assistance system that assists a Do It Yourself (DIY) handyman in carrying out DIY projects. The assistant, based on various AI and dialog management capabilities, generates a sequence of detailed instructions that users may just follow or adapt according to their individual preferences. It features explanation capabilities as well as pro-active support based on communication with the user as well as with the involved tools. We report on the project’s main achievements, including the findings of various empirical studies conducted in various development stages of the prototype.

• Gregor Behnke.
  Block Compression and Invariant Pruning for SAT-based Totally-Ordered HTN Planning.
  In Proceedings of the 31st International Conference on Automated Planning and Scheduling (ICAPS 2021). 2021.
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  Translations into propositional logic are currently one of the most efficient techniques for solving Totally-Ordered HTN planning problems. The current encodings iterate over the maximum allowed depth of decomposition. Given this depth, they compute a tree that represents all possible decompositions up to this depth. Based on this tree, a formula in propositional logic is created. We show that much of the computed tree is actually useless as it cannot possibly belong to a solution. We provide a technique for removing (parts of) these useless structures using state invariants. We further show that is often not necessary to encode all leafs of this tree as separate timesteps, as the prior encodings did. Instead, we can compress the leafs into blocks and encode all leafs of a block as one timestep. We show that these changes provide an improvement over the state-of-the-art in HTN planning.

• Daniel Höller and Gregor Behnke.
  Loop Detection in the PANDA Planning System.
  In Proceedings of the 31st International Conference on Automated Planning and Scheduling (ICAPS 2021). 2021.
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  The International Planning Competition (IPC) in 2020 was the first one for a long time to host tracks on Hierarchical Task Network (HTN) planning. HyperTensioN, the winner of the tack on totally-ordered problems, comes with an interesting technique: it stores parts of the decomposition path in the state to mark expanded tasks and forces its depth first search to leave recursive structures in the hierarchy. This can be seen as a form of loop detection (LD) -- a technique that is not very common in HTN planning. This might be due to the spirit of encoding enough advice in the model to find plans (so that loop detection is simply not necessary), or because it becomes a computationally hard task in the general (i.e. partially-ordered) setting. We integrated several approximate and exact techniques for LD into the progression search of the HTN planner PANDA. We test our techniques on the benchmark set of the IPC 2020. Both in the partial ordered and total ordered track, PANDA with LD performs better than the respective winner of the competition.

• David Speck, David Borukhson, Robert Mattmüller and Bernhard Nebel.
  On the Compilability and Expressive Power of State-Dependent Action Costs.
  In Proceedings of the 31st International Conference on Automated Planning and Scheduling (ICAPS 2021), pp. 358-366. 2021.
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  While state-dependent action costs are practically relevant and have been studied before, it is still unclear if they are an essential feature of planning tasks. In this paper, we study to what extent state-dependent action costs are an essential feature by analyzing under which circumstances they can be compiled away. We give a comprehensive classification for combinations of action cost functions and possible cost measures for the compilations.

  Our theoretical results show that if both task sizes and plan lengths are to be preserved polynomially, then the boundary between compilability and non-compilability lies between FP and FPSPACE computable action cost functions (under a mild assumption on the polynomial hierarchy). Preserving task sizes polynomially and plan lengths linearly at the same time is impossible.

• David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller and Marius Lindauer.
  Learning Heuristic Selection with Dynamic Algorithm Configuration.
  In Proceedings of the 31st International Conference on Automated Planning and Scheduling (ICAPS 2021), pp. 597-605. 2021.
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  A key challenge in satisficing planning is to use multiple heuristics within one heuristic search. An aggregation of multiple heuristic estimates, for example by taking the maximum, has the disadvantage that bad estimates of a single heuristic can negatively affect the whole search. Since the performance of a heuristic varies from instance to instance, approaches such as algorithm selection can be successfully applied. In addition, alternating between multiple heuristics during the search makes it possible to use all heuristics equally and improve performance. However, all these approaches ignore the internal search dynamics of a planning system, which can help to select the most useful heuristics for the current expansion step. We show that dynamic algorithm configuration can be used for dynamic heuristic selection which takes into account the internal search dynamics of a planning system. Furthermore, we prove that this approach generalizes over existing approaches and that it can exponentially improve the performance of the heuristic search. To learn dynamic heuristic selection, we propose an approach based on reinforcement learning and show empirically that domain-wise learned policies, which take the internal search dynamics of a planning system into account, can exceed existing approaches.

• Dominik Drexler, Jendrik Seipp and David Speck.
  Subset-Saturated Transition Cost Partitioning.
  In Proceedings of the 31st International Conference on Automated Planning and Scheduling (ICAPS 2021), pp. 131-139. 2021.
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  Cost partitioning admissibly combines the information from multiple heuristics for optimal state-space search. One of the strongest cost partitioning algorithms is saturated cost partitioning. It considers the heuristics in sequence and assigns to each heuristic the minimal fraction of the remaining costs that are needed for preserving all heuristic estimates. Saturated cost partitioning has recently been generalized in two directions: first, by allowing to use different costs for the transitions induced by the same operator, and second, by preserving the heuristic estimates for only a subset of states. In this work, we unify these two generalizations and show that the resulting subset-saturated transition cost partitioning algorithm usually yields stronger heuristics than the two generalizations by themselves.

• Thorsten Engesser, Robert Mattmüller, Bernhard Nebel and Michael Thielscher.
  Game description language and dynamic epistemic logic compared.
  Artificial Intelligence 292. 2021.
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  Several different frameworks have been proposed to model and reason about knowledge in dynamic multi-agent settings, among them the logic-programming-based game description language GDL-III and dynamic epistemic logic (DEL). GDL-III and DEL have complementary strengths and weaknesses in terms of ease of modeling and simplicity of semantics. In this paper, we formally study the expressiveness of GDL-III vs. DEL. We clarify the commonalities and differences between those languages, demonstrate how to bridge the differences where possible, and identify large fragments of GDL-III and DEL that are equivalent in the sense that they can be used to encode games or planning tasks that admit the same legal action sequences. We prove the latter by providing translations between those fragments of GDL-III and DEL.

• Daniel Höller, Gregor Behnke, Pascal Bercher and Susanne Biundo.
  The PANDA Framework for Hierarchical Planning.
  KI - Künstliche Intelligenz. 2021.
  (Online)
  

• David Speck and Michael Katz.
  Symbolic Search for Oversubscription Planning.
  In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), pp. 11972-11980. 2021.
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  The objective of optimal oversubscription planning is to find a plan that yields an end state with a maximum utility while keeping plan cost under a certain bound. In practice, the situation occurs whenever a large number of possible, often competing goals of varying value exist, or the resources are not sufficient to achieve all goals. In this paper, we investigate the use of symbolic search for optimal oversubscription planning. Specifically, we show how to apply symbolic forward search to oversubscription planning tasks and prove that our approach is sound, complete and optimal. An empirical analysis shows that our symbolic approach favorably competes with explicit state-space heuristic search, the current state of the art for oversubscription planning.

• Gregor Behnke and David Speck.
  Symbolic Search for Optimal Total-Order HTN Planning.
  In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), p. 11744–11754. 2021.
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  Symbolic search has proven to be a useful approach to optimal classical planning. In Hierarchical Task Network (HTN) planning, however, there is little work on optimal planning. One reason for this is that in HTN planning, most algorithms are based on heuristic search, and admissible heuristics have to incorporate the structure of the task network in order to be informative. In this paper, we present a novel approach to optimal (totally-ordered) HTN planning, which is based on symbolic search. An empirical analysis shows that our symbolic approach outperforms the current state of the art for optimal totally-ordered HTN planning.

• Patrick Caspari, Robert Mattmüller and Tim Schulte.
  A Framework to Prove Strong Privacy in Multi-Agent Planning.
  In Proceedings of the 6th Workshop on Distributed and Multi-Agent Planning (DMAP 2020), pp. 32-39. 2020.
  (PDF)
  

• David Speck, André Biedenkapp, Frank Hutter, Robert Mattmüller and Marius Lindauer.
  Learning Heuristic Selection with Dynamic Algorithm Configuration.
  In Proceedings of the Workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL 2020), p. 61–69. 2020.
  Superseded by the ICAPS 2021 paper by the same authors.
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  A key challenge in satisficing planning is to use multiple heuristics within one heuristic search. An aggregation of multiple heuristic estimates, for example by taking the maximum, has the disadvantage that bad estimates of a single heuristic can negatively affect the whole search. Since the performance of a heuristic varies from instance to instance, approaches such as algorithm selection can be successfully applied. In addition, alternating between multiple heuristics during the search makes it possible to use all heuristics equally and improve performance. However, all these approaches ignore the internal search dynamics of a planning system, which can help to select the most helpful heuristics for the current expansion step. We show that dynamic algorithm configuration can be used for dynamic heuristic selection which takes into account the internal search dynamics of a planning system. Furthermore, we prove that this approach generalizes over existing approaches and that it can exponentially improve the performance of the heuristic search. To learn dynamic heuristic selection, we propose an approach based on reinforcement learning and show empirically that domain-wise learned policies, which take the internal search dynamics of a planning system into account, can exceed existing approaches in terms of coverage.

• Dominik Drexler, David Speck and Robert Mattmüller.
  Subset-Saturated Transition Cost Partitioning for Optimal Classical Planning.
  In Proceedings of the 12th Workshop on Heuristics and Search for Domain-Independent Planning (HSDIP 2020), p. 23–31. 2020.
  Superseded by the ICAPS 2021 paper "Subset-Saturated Transition Cost Partitioning".
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  Cost partitioning admissibly combines the information from multiple heuristics for state-space search. We use a greedy method called saturated cost partitioning that considers the heuristics in sequence and assigns the minimal fraction of the remaining costs that it needs to preserve the heuristic estimates. In this work, we address the problem of using more expressive transition cost functions with saturated cost partitioning to obtain stronger heuristics. Our contribution is subset-saturated transition cost partitioning that combines the concepts of using transition cost functions and prioritizing states that look more important during the search. Our empirical evaluation shows that this approach still causes too much computational overhead but leads to more informed heuristics.

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