The 2023 International Planning Competition
Open accessAbstract
Abstract In this article, we present an overview of the 2023 International Planning Competition. It featured five distinct tracks designed to assess cutting‐edge methods and explore the frontiers of planning within these settings: the classical (deterministic) track, the numeric track, the Hierarchical Task Networks (HTN) track, the learning track, and the probabilistic and reinforcement learning track. Each of these tracks evaluated planning methodologies through one or more subtracks, with the goal of pushing the boundaries of current planner performance. To achieve this objective, the competition introduced a combination of well‐established challenges and entirely novel ones. Within this article, each track offers an exploration of its historical context, justifies its relevance within the planning landscape, discusses emerging domains and trends, elucidates the evaluation methodology, and ultimately presents the results.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Planetarium contains a dataset of 145,918 text-to-PDDL pairs derived from the International Planning Competition, across 73 unique state combinations.
“along with a dataset of 145,918 text-to-PDDL pairs across 73 unique state combinations with varying levels of difficulty.”
From Planetarium: A Rigorous Benchmark for Translating Text to Structured Planning Languages (2025) · cites this paper as Taitler et al., 2024
The Classical Track history and motivation section discusses IPC planning domains and PDDL, but not Planetarium or a text-to-PDDL dataset. Searches for Planetarium, text-to-PDDL pairs, 145,918, and 73 state combinations found no passage establishing the claimed dataset size, composition, or derivation from the competition. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Singularity: Scientific containers for mobility of compute
Kurtzer, G. M., Sochat, V., & Bauer, M. W. (2017) · PLOS ONE · doi:10.1371/journal.pone.0177459 · Cited by 2,740 · see citing works
Sequential Model-Based Optimization for General Algorithm Configuration
Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-25566-3_40 · Cited by 2,469 · see citing works
Automated Planning: Theory and Practice
Ghallab, M., Nau, D. S., & Traverso, P. (2004) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 1,519 · see citing works
(1994) · Cited by 625 · see citing works
The 1998 AI Planning Systems Competition
McDermott, D. M. (2000) · The AI Magazine · doi:10.1609/aimag.v21i2.1506 · Cited by 294 · see citing works
The 3rd International Planning Competition: Results and Analysis
Long, D. P., & Fox, M. (2003) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1240 · Cited by 289 · see citing works
Fundamentals of Airplane Flight Mechanics
Hull, D. G. (2007) · Springer eBooks · doi:10.1007/978-3-540-46573-7 · Cited by 218 · see citing works
The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks
Richter, S., & Westphal, M. (2010) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2972 · Cited by 205 · see citing works
The First Probabilistic Track of the International Planning Competition
Younes, H. L. S., Littman, M. L., Weissman, D., & Asmuth, J. C. (2005) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1880 · Cited by 133 · see citing works
Optimal layout synthesis for quantum computing
Tan, B., & Cong, J. (2020) · Proceedings of the 39th International Conference on Computer-Aided Design · doi:10.1145/3400302.3415620 · Cited by 126 · see citing works
complexity, logic, and recursion theory
Sorbi, A. (2019) · CRC Press eBooks · doi:10.1201/9780429187490 · Cited by 123 · see citing works
The Good Old Davis-Putnam Procedure Helps Counting Models
Birnbaum, E., & Lozinskii, E. L. (1999) · Journal of Artificial Intelligence Research · doi:10.1613/jair.601 · Cited by 120 · see citing works
The 2014 International Planning Competition: Progress and Trends
Vallati, M., Chrpa, L., Grześ, M., McCluskey, T. L., Roberts, M., & Sanner, S. (2015) · AI Magazine · doi:10.1609/aimag.v36i3.2571 · Cited by 112 · see citing works
Self-citationA Survey of the Seventh International Planning Competition
Coles, A., Coles, A., García‐Olaya, Á., Jiménez, S., López, C. L., Sanner, S., & Yoon, S. (2012) · AI Magazine · doi:10.1609/aimag.v33i1.2392 · Cited by 109 · see citing works
Self-citationMixed-Initiative Planning and Execution for Multiple Drones in Search and Rescue Missions
Bevacqua, G., Cacace, J., Finzi, A., & Lippiello, V. (2015) · Proceedings of the International Conference on Automated Planning and Scheduling · doi:10.1609/icaps.v25i1.13700 · Cited by 101 · see citing works
Decidability and Undecidability Results for Planning with Numerical State Variables.
Helmert, M. (2002) · Cited by 98 · see citing works
Practical solution techniques for first-order MDPs
Sanner, S., & Boutilier, C. E. (2008) · Artificial Intelligence · doi:10.1016/j.artint.2008.11.003 · Cited by 81 · see citing works
Self-citationA Survey on Hierarchical Planning – One Abstract Idea, Many Concrete Realizations
Bercher, P., Alford, R., & Höller, D. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/875 · Cited by 76 · see citing works
Self-citationHDDL: An Extension to PDDL for Expressing Hierarchical Planning Problems
Höller, D., Behnke, G., Bercher, P., Biundo, S., Fiorino, H., Pellier, D., & Alford, R. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i06.6542 · Cited by 72 · see citing works
Self-citationASNets: Deep Learning for Generalised Planning
Toyer, S., Thiébaux, S., Trevizan, F., & Xie, L. (2020) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11633 · Cited by 48 · see citing works
Alford, R., Bercher, P., & Aha, D. W. (2015) · Proceedings of the International Conference on Automated Planning and Scheduling · doi:10.1609/icaps.v25i1.13721 · Cited by 48 · see citing works
Self-citationSymbolic Dynamic Programming for Discrete and Continuous State MDPsPreprint
Sanner, S., Delgado, K. V., & de Barros, L. N. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1202.3762 · Cited by 48 · see citing works
Self-citationAlford, R., Behnke, G., Höller, D., Bercher, P., Biundo, S., & Aha, D. W. (2016) · Proceedings of the International Conference on Automated Planning and Scheduling · doi:10.1609/icaps.v26i1.13765 · Cited by 39 · see citing works
Self-citationThe Seventh Answer Set Programming Competition: Design and Results
GEBSER, M., MARATEA, M., & Ricca, F. (2019) · Theory and Practice of Logic Programming · doi:10.1017/s1471068419000061 · Cited by 39 · see citing works
The first learning track of the international planning competition
Fern, A., Khardon, R., & Tadepalli, P. V. (2011) · Machine Learning · doi:10.1007/s10994-011-5234-y · Cited by 38 · see citing works
Subgoaling Techniques for Satisficing and Optimal Numeric Planning
Scala, E., Haslum, P., Thiébaux, S., & Ramírez, M. (2020) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11875 · Cited by 37 · see citing works
Self-citationMultirobot Coordination for Space Exploration
Yliniemi, L., Agogino, A., & Tumer, K. (2014) · AI Magazine · doi:10.1609/aimag.v35i4.2556 · Cited by 36 · see citing works
Landmarks for Numeric Planning Problems
Scala, E., Haslum, P., Magazzeni, D., & Thiébaux, S. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/612 · Cited by 29 · see citing works
Self-citationLilotane: A Lifted SAT-based Approach to Hierarchical Planning
Schreiber, D. (2021) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.12520 · Cited by 29 · see citing works
Self-citationOptimal Layout Synthesis for Quantum Circuits as Classical Planning
Shaik, I., & van de Pol, J. (2023) · 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD) · doi:10.1109/iccad57390.2023.10323924 · Cited by 24 · see citing works
On Succinct Groundings of HTN Planning Problems
Behnke, G., Höller, D., Schmid, A., Bercher, P., & Biundo, S. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i06.6529 · Cited by 24 · see citing works
Self-citationDeep Reactive Policies for Planning in Stochastic Nonlinear Domains
Bueno, T. P., de Barros, L. N., Mauá, D. D., & Sanner, S. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33017530 · Cited by 24 · see citing works
Self-citationLinear and Integer Programming-Based Heuristics for Cost-Optimal Numeric Planning
Piacentini, C., Castro, M. P., Ciré, A. A., & Beck, J. C. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12082 · Cited by 23 · see citing works
Learning How to Ground a Plan – Partial Grounding in Classical Planning
Gnad, D., Torralba, Á., Domínguez, M. A., Areces, C., & Bustos, F. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33017602 · Cited by 23 · see citing works
Optimal Planning Modulo Theories
Leofante, F., Giunchiglia, E., Ábrahám, E., & Tacchella, A. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/571 · Cited by 22 · see citing works
Alice in DIY wonderland or: Instructing novice users on how to use tools in DIY projects
Behnke, G., Schiller, M. R. G., Kraus, M., Bercher, P., Schmautz, M., Dorna, M., Dambier, M., Minker, W., Glimm, B., & Biundo, S. (2019) · AI Communications · doi:10.3233/aic-180604 · Cited by 20 · see citing works
Self-citationOn Guiding Search in HTN Planning with Classical Planning Heuristics
Höller, D., Bercher, P., Behnke, G., & Biundo, S. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/857 · Cited by 20 · see citing works
Self-citationOptimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching ApproachPreprint
Mladenov, M., Creager, E., Ben-Porat, O., Swersky, K., Zemel, R. S., & Boutilier, C. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.00104 · Cited by 18 · see citing works
Learning Generalized Policies without Supervision Using GNNs
Ståhlberg, S., Bonet, B., & Geffner, H. (2022) · Principles of Knowledge Representation and Reasoning · doi:10.24963/kr.2022/49 · Cited by 16 · see citing works
Höller, D. (2021) · Proceedings of the International Conference on Automated Planning and Scheduling · doi:10.1609/icaps.v31i1.15958 · Cited by 11 · see citing works
Symbolic Search for Optimal Total-Order HTN Planning
Behnke, G., & Speck, D. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i13.17396 · Cited by 11 · see citing works
Self-citationHyperTensioN: A three-stage compiler for planning
Meneguzzi, F. R., Magnaguagno, M. C., & de Silva, L. (2020) · Cited by 10 · see citing works
Making Translations to Classical Planning Competitive with Other HTN Planners
Behnke, G., Pollitt, F., Höller, D., Bercher, P., & Alford, R. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i9.21203 · Cited by 10 · see citing works
Self-citationLoop Detection in the PANDA Planning System
Höller, D., & Behnke, G. (2021) · Proceedings of the International Conference on Automated Planning and Scheduling · doi:10.1609/icaps.v31i1.15959 · Cited by 9 · see citing works
Self-citationOn the Compilability of Bounded Numeric Planning
Gigante, N., & Scala, E. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/593 · Cited by 8 · see citing works
Self-citationScaling-Up Generalized Planning as Heuristic Search with Landmarks
Segovia‐Aguas, J., Celorrio, S. J., Sebastiá, L., & Jönsson, A. (2022) · Proceedings of the International Symposium on Combinatorial Search · doi:10.1609/socs.v15i1.21765 · Cited by 8 · see citing works
Self-citationThe LM-Cut Heuristic Family for Optimal Numeric Planning with Simple Conditions
Kuroiwa, R., Shleyfman, A., Piacentini, C., Castro, M. P., & Beck, J. C. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.14034 · Cited by 8 · see citing works
Block Compression and Invariant Pruning for SAT-based Totally-Ordered HTN Planning
Behnke, G. (2021) · Proceedings of the International Conference on Automated Planning and Scheduling · doi:10.1609/icaps.v31i1.15943 · Cited by 8 · see citing works
Self-citationLearning Hierarchical Policies by Iteratively Reducing the Width of Sketch Rules
Drexler, D., Seipp, J., & Geffner, H. (2023) · Principles of Knowledge Representation and Reasoning · doi:10.24963/kr.2023/21 · Cited by 6 · see citing works
Self-citationNovelty and Lifted Helpful Actions in Generalized Planning
Lei, C., Lipovetzky, N., & Ehinger, K. A. (2023) · Proceedings of the International Symposium on Combinatorial Search · doi:10.1609/socs.v16i1.27294 · Cited by 4 · see citing works
OMTPlan: A Tool for Optimal Planning Modulo Theories
Leofante, F. (2023) · Journal on Satisfiability Boolean Modeling and Computation · doi:10.3233/sat-220001 · Cited by 4 · see citing works
DiSProD: Differentiable Symbolic Propagation of Distributions for Planning
Chatterjee, P., Chapagain, A., Chen, W., & Khardon, R. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/591 · Cited by 2 · see citing works
Relaxing non-interference requirements in parallel plans
Bofill, M., Espasa, J., & Villaret, M. (2019) · Logic Journal of IGPL · doi:10.1093/jigpal/jzz026 · Cited by 2 · see citing works
Self-citation
Flagged first, then by citations
1 of the 54 references couldn’t be matched to a known paper.
Research works printed but not linked: 11
- Behnke, G., D. Höller, and P. Bercher, eds. 2021. Proceedings of the 10th International Planning Competition: Planner and Domain Abstracts -Hierarchical Task Network (HTN) Planning Track (IPC 2020). https://ipc2020.hierarchical- task.net/publications/IPC2020Booklet.pdf.
- Brockman, G., V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba. 2016. "Openai Gym." arXiv preprint arXiv:1606.01540. · arXiv
- Fernandez-Olivares, J., I. Vellido, and L. Castillo. 2021. "Address- ing HTN Planning with Blind Depth First Search." In Proceedings of 10th International Planning Competition: Planner and Domain Abstracts -Hierarchical Task Network (HTN) Planning Track (IPC 2020), 1-4.
- Gebser, M., M. Maratea, and F. Ricca. 2016. "What's Hot in the Answer Set Programming Competition." In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12- 17, 2016, Phoenix, Arizona, USA, eds. D. Schuurmans and M. P. Wellman, 4327-29. AAAI Press.
- C. A. C. Coello, 507-23. Springer.
- PMLR.
- Olz, C., E. Wierzba, P. Bercher, and F. Lindner. 2021. "Towards Improving the Comprehension of HTN Planning Domains by Means of Preconditions and Effects of Compound Tasks." In Proceedings of the 10th Workshop on Knowledge Engineering for Planning and Scheduling, KEPS.
- Seipp, J. 2019. "Planner Metrics Should Satisfy Independence of Irrel- evant Alternatives." In ICAPS 2019 Workshop on the International Planning Competition (WIPC), 40-41.
- Sharma, V., D. Arora, Mausam, and P. Singla. 2023. "SymNet 3.0: Exploiting Long-Range Influences in Learning Generalized Neural Policies for Relational MDPs." In Proceedings of the Thirty- Ninth Conference on Uncertainty in Artificial Intelligence, eds. R. J. Evans and I. Shpitser, volume 216 of Proceedings of Machine Learning Research, 1921-31. PMLR.
- Taitler, A., M. Gimelfarb, S. Gopalakrishnan, M. Mladenov, X. Liu, and S. Sanner. 2023. "pyRDDLGym: From RDDL to Gym Envi- ronments." In ICAPS 2023 Workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (PRL).
- Vellido, I., J. Fdez-Olivares, and R. Pérez. 2020. "A Knowledge Based Process for the Generation of HTN Domains from VGDL Video Game Descriptions." In ICAPS2020 Workshop on Knowledge Engineering for Planning and Scheduling, 11-20.
Cited by
Sorted by citations
The Sixth Answer Set Programming Competition
Gebser, M., Marco Maratea, A., & Ricca, F. (2017) · Journal of Artificial Intelligence Research · doi:10.1613/jair.5373 · Cited by 61 · see citing works
Reflections on Artificial Intelligence for Humanity
Braunschweig, B., & Ghallab, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69128-8 · Cited by 44 · see citing works
Construction of New Medicines via Game Proof Search
Heifets, A., & Jurišica, I. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v26i1.8331 · Cited by 35 · see citing works
Better Parameter-Free Anytime Search by Minimizing Time Between Solutions
Thayer, J. T., Benton, J., & Helmert, M. (2021) · Proceedings of the International Symposium on Combinatorial Search · doi:10.1609/socs.v3i1.18242 · Cited by 21 · see citing works
Unified Planning: Modeling, manipulating and solving AI planning problems in Python
Micheli, A., Bit-Monnot, A., Röger, G., Scala, E., Valentini, A., Framba, L., Rovetta, A., Trapasso, A., Bonassi, L., Gerevini, A., et al. (2024) · SoftwareX · doi:10.1016/j.softx.2024.102012 · Cited by 17 · see citing works
Self-citationBDDs Strike Back (in AI Planning)
Edelkamp, S., Kissmann, P., & Torralba, Á. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9349 · Cited by 16 · see citing works
Engineering a Conformant Probabilistic Planner
Onder, N., Whelan, G. C., & Li, L. (2006) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1701 · Cited by 16 · see citing works
A resource based framework for planning and replanning
van der Krogt, R., Weerdt, M. M. D., & Witteveen, C. (2004) · IEEE/WIC/ACM International Conference on Intelligent Agent Technology · doi:10.1109/iat.2003.1241075 · Cited by 15 · see citing works
On modeling planning problems in tabled logic programming
Barták, R., Agostino Dovier, A., & Zhou, N. (2015) · International Symposium on Principles and Practice of Declarative Programming (PPDP) · doi:10.1145/2790449.2790521 · Cited by 10 · see citing works
Automatic construction of optimal static sequential portfolios for AI planning and beyond
Núñez, S., Borrajo, D., & López, C. L. (2015) · Artificial Intelligence · doi:10.1016/j.artint.2015.05.005 · Cited by 9 · see citing works
Planetarium: A Rigorous Benchmark for Translating Text to Structured Planning Languages
Zuo, M., Velez, F. P., Li, X., Littman, M., & Bach, S. (2025) · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · doi:10.18653/v1/2025.naacl-long.560 · Cited by 5 · see citing works
“This algo-rithm transforms the PDDL code into scene graphs, computes an expansion of the goal states for both PDDL problems, and then performs isomorphism checks between the graphs.”
Using Machine Learning for Decreasing State Uncertainty in Planning
Krivić, S., Cashmore, M., Magazzeni, D., Szedmák, S., & Piater, J. (2020) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11567 · Cited by 2 · see citing works
Exploiting Macro-Actions in Learning GPT-Based General Planning Policies
Tummolo, M., Rossetti, N., Chrpa, L., Serina, I., & Gerevini, A. (2025) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai66417.2025.00133 · Cited by 1 · see citing works
Leveraging AI Planning Models for the Water Management of the Red River Basin in Vietnam
Aineto, D., Battisti, M., Nguyen, H. Y., An, N. L., Ranzi, R., Scala, E., & Serina, I. (2025) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-96-7749-8_37 · Cited by 1 · see citing works
Self-citationOn the Generalization Gap in LLM Planning: Tests and Verifier-Reward RL
Belcamino, V., Attolino, N., Capitanelli, A., & Mastrogiovanni, F. (2026) · IEEE/ASME International Conference on Advanced Intelligent Mechatronics · doi:10.1109/aim65483.2026.11657905 · Cited by 1 · see citing works
Geng, L., & Chang, E. Y. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770854.3785692 · Cited by 20 · see citing works
A hybrid approach for expressive numeric and temporal planning with control parameters
Sapena, Ó., Onaindía, E., & Marzal, E. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122820 · Cited by 1 · see citing works
Efficient HTN to STRIPS Encodings for Concurrent Planning
Cavrel, N., Pellier, D., & Fiorino, H. (2023) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai59109.2023.00144 · Cited by 1 · see citing works
Decision-Making in an Energy System: A Knowledge-Based Solution
Boukerche, K., & Lounis, H. (2006) · International Conference on Systems Engineering · doi:10.1109/icseng.2005.33
(2004) · Elsevier eBooks · doi:10.1016/b978-155860856-6/50038-7
Gimelfarb, M., Taitler, A., & Sanner, S. (2024) · Proceedings of the International Conference on Automated Planning and Scheduling · doi:10.1609/icaps.v34i1.31480
Self-citationA Lazy Approach to Neural Numerical Planning with Control Parameters
Heesch, R., Cimatti, A., Ehrhardt, J., Diedrich, A., & Niggemann, O. (2024) · Frontiers in artificial intelligence and applications · doi:10.3233/faia241000
AutoGT: Automatic Generation of Game Trees for Algorithm Benchmarking
Karwowski, J., Świechowski, M., & Mańdziuk, J. (2025) · IEEE Transactions on Games · doi:10.1109/tg.2025.3566891
Automating Transfer of Robot Task Plans Using Functorial Data Migrations
Aguinaldo, A., Patterson, E., & Regli, W. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3586549
Dynamic machine-level planning for Plug & Produce manufacturing
Nilsson, A., Danielsson, F., & Lennartson, B. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-026-19253-5
- Authors
- Taitler, A., Alford, R., Espasa, J., Behnke, G., Fišer, D., Gimelfarb, M., Pommerening, F., Sanner, S., Scala, E., Schreiber, D., Segovia‐Aguas, J., & Seipp, J.
- Affiliations
- University of Toronto (CA)
- Mitre (United States) (US)
- University of St Andrews (GB)
- University of Amsterdam (NL)
- Saarland University (DE)
- University of Basel (CH)
- University of Brescia (IT)
- Karlsruhe Institute of Technology (DE)
- Universitat Pompeu Fabra (ES)
- Linköping University (SE)
- Corresponding author
- Taitler, Ayal
- ORCID
- Venue
- AI Magazine
- Publisher
- Wiley
- Published
- 2024-04-05
- Volume
- 45
- Issue
- 2
- Pages
- 280-296
- ISSN
- 0738-4602 (ISSN-L)
- 2371-9621
- OpenAlex
- W4393988223
- Cited by
- 24
- References
- 54
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 3 · 2023: 2 · 2021: 3 · 2020: 1 · 2017: 1 · 2015: 3
- FWCI
- 1.48
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- AI Magazine (publishedVersion, cc-by)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (publishedVersion, cc-by)
- Repository KITopen (Karlsruhe Institute of Technology) (submittedVersion, cc-by)
- KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion)
- UvA-DARE (University of Amsterdam) (submittedVersion, cc-by)
- e-Archivo (Carlos III University of Madrid) (submittedVersion, cc-by-nc-nd)
- Institutional Research Information System (Università degli Studi di Brescia) (submittedVersion)
- St Andrews Research Repository (St Andrews Research Repository) (submittedVersion, other-oa)
- KITopen (cc-by)
- Funders
- Vetenskapsrådet
- University Of Edinburgh
- Engineering and Physical Sciences Research Council (EP/P020267/1)
- Natural Sciences and Engineering Research Council of Canada
- European Research Council (882500)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Computer Networks and Communications › Constraint Satisfaction and Optimization
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Keywords
- International Planning Competition, automated planning, planning competitions, hierarchical task networks, probabilistic planning, learning-based planning, deterministic planning, numeric planning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
