The deterministic part of the seventh International Planning Competition
Open accessAbstract
The International Planning Competition is organized in the context of the International Conference on Automated Planning and Scheduling (ICAPS) and it is considered a reference source for the planning and scheduling community. The competition is typically organized every two years and deals with relevant issues for the community such as the definition of evaluation standards, the publication of benchmarks and the collection and dissemination of data about state-of-the-art planners. This paper focuses on the deterministic part, the longest-running part of the International Planning Competition. The paper describes its format, the participants, the selection of benchmarks and the generated results accompanied with analysis from different perspectives. The paper also examines the results of a brand new track created to explore the potential of planners that exploit the power of multi-core processors. Overall, the results of the competition indicate significant progress with respect to previous competitions, but they also reveal that some issues remain open and need further research, such as the coverage of temporal planners when concurrency is required and the performance in the multi-core track. As a novelty, all the data and the software generated for running the competition have been made publicly available allowing researchers to reproduce the competition and to carry out different analysis of the results.
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The equivalence algorithm is efficient for three domains: Blocks World, Gripper, and a slightly simplified Floor Tile.
“We show how to make this algorithm efficient for three domains: Blocks World, Gripper, and a slightly simplified Floor Tile (McDermott, 2000;Linares López et al., 2015).”
From Planetarium: A Rigorous Benchmark for Translating Text to Structured Planning Languages (2025) · cites this paper as Linares López et al., 2015
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Cites
Graph-Based Algorithms for Boolean Function Manipulation
Bryant (1986) · IEEE Transactions on Computers · doi:10.1109/tc.1986.1676819 · Cited by 8,941 · see citing works
Cohen, J. (1992) · Psychological Bulletin · doi:10.1037//0033-2909.112.1.155 · Cited by 8,771 · see citing works
The FF Planning System: Fast Plan Generation Through Heuristic Search
Hoffmann, J., & Nebel, B. (2001) · Journal of Artificial Intelligence Research · doi:10.1613/jair.855 · Cited by 1,840 · see citing works
PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains
Fox, M., & Long, D. (2003) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1129 · Cited by 1,741 · see citing works
Scientific method: Statistical errors
Nuzzo, R. (2014) · Nature · doi:10.1038/506150a · Cited by 1,536 · see citing works
Where the really hard problems are
Cheeseman, P., Kanefsky, B., & Taylor, W. M. (1991) · Cited by 1,015 · see citing works
The computational complexity of propositional STRIPS planning
Bylander, T. (1994) · Artificial Intelligence · doi:10.1016/0004-3702(94)90081-7 · Cited by 866 · see citing works
The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks
Richter, S., & Westphal, M. (2010) · Cited by 510 · see citing works
Parameter Estimation in Large Dynamic Paired Comparison Experiments
Glickman, M. E. (1999) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/1467-9876.00159 · Cited by 364 · see citing works
Gerevini, A., Haslum, P., Long, D. P., Saetti, A., & Dimopoulos, Y. (2008) · Artificial Intelligence · doi:10.1016/j.artint.2008.10.012 · 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
Flexible abstraction heuristics for optimal sequential planning
Helmert, M., Haslum, P., & Hoffmann, J. (2007) · ANU Open Research (Australian National University) · Cited by 208 · 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
VAL: automatic plan validation, continuous effects and mixed initiative planning using PDDL
Howey, R., Long, D., & Fox, M. (2005) · 16th IEEE International Conference on Tools with Artificial Intelligence · doi:10.1109/ictai.2004.120 · Cited by 194 · see citing works
Temporal planning with mutual exclusion reasoning
Smith, D. E., & Weld, D. S. (1999) · Cited by 182 · see citing works
The Deterministic Part of IPC-4: An Overview
Hoffmann, J., & Edelkamp, S. (2005) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1677 · Cited by 145 · see citing works
When is temporal planning really temporal
Cushing, W., Subbarao Kambhampati, B., & Weld, D. S. (2007) · Cited by 144 · see citing works
The International SAT Solver Competitions
Järvisalo, M., Berre, D. L., Roussel, O., & Simon, L. (2012) · AI Magazine · doi:10.1609/aimag.v33i1.2395 · Cited by 127 · see citing works
Where Ignoring Delete Lists Works: Local Search Topology in Planning Benchmarks
Hoffmann, J. (2003) · Cited by 119 · see citing works
Complexity results for standard benchmark domains in planning
Helmert, M. (2003) · Artificial Intelligence · doi:10.1016/s0004-3702(02)00364-8 · Cited by 116 · see citing works
Planning Through Stochastic Local Search and Temporal Action Graphs in LPG
Gerevini, A., Saetti, A., & Serina, I. (2003) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1183 · Cited by 115 · see citing works
A 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-citationHelmert, M., & Röger, G. (2008) · Cited by 103 · see citing works
Best-First Heuristic Search for Multicore Machines
Burns, E., Lemons, S., Ruml, W., & Zhou, R. (2010) · Journal of Artificial Intelligence Research · doi:10.1613/jair.3094 · Cited by 82 · see citing works
Baier, J. A., & McIlraith, S. A. (2008) · AI Magazine · doi:10.1609/aimag.v29i4.2204 · Cited by 81 · see citing works
Complexity of concurrent temporal planning
Rintanen, J. (2007) · Cited by 66 · see citing works
Soft Goals Can Be Compiled Away
Keyder, E., & Geffner, H. (2009) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2857 · Cited by 61 · see citing works
Engineering Benchmarks for Planning: the Domains Used in the Deterministic Part of IPC-4
Hoffmann, J., Edelkamp, S., Thiébaux, S., Englert, R. A., Liporace, F. S., & Trüg, S. (2006) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1982 · Cited by 60 · see citing works
Analyzing Search Topology Without Running Any Search: On the Connection Between Causal Graphs and h+
Hoffmann, J. (2011) · Journal of Artificial Intelligence Research · doi:10.1613/jair.3276 · Cited by 59 · see citing works
New complexity results for classical planning benchmarks
Helmert, M. (2006) · Cited by 56 · see citing works
Evaluation of a simple, scalable, parallel best-first search strategy
Kishimoto, A., Fukunaga, A. S., & Botea, A. (2012) · Artificial Intelligence · doi:10.1016/j.artint.2012.10.007 · Cited by 45 · see citing works
Valenzano, R., Sturtevant, N., Schaeffer, J., Buro, K., & Kishimoto, A. (2021) · Proceedings of the International Conference on Automated Planning and Scheduling · doi:10.1609/icaps.v20i1.13423 · Cited by 45 · see citing works
“Distance”? Who Cares? Tailoring Merge-and-Shrink Heuristics to Detect Unsolvability
Jörg, H., Kissmann, P., & Torralba, Á. (2014) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-61499-419-0-441 · Cited by 44 · see citing works
Learning from planner performance
Roberts, M., & Howe, A. E. (2008) · Artificial Intelligence · doi:10.1016/j.artint.2008.11.009 · Cited by 38 · see citing works
Automated Planning for Liner Shipping Fleet Repositioning
Tierney, K., Coles, A., Coles, A., Kroer, C., Britt, A., & Jensen, R. M. (2012) · Proceedings of the International Conference on Automated Planning and Scheduling · doi:10.1609/icaps.v22i1.13500 · Cited by 36 · see citing works
The 2011 International Planning Competition
García‐Olaya, Á., Jiménez, S., & López, C. L. (2011) · Cited by 36 · see citing works
Self-citationPlan-based Policies for Efficient Multiple Battery Load Management
Fox, M., Long, D., & Magazzeni, D. (2012) · Journal of Artificial Intelligence Research · doi:10.1613/jair.3643 · Cited by 32 · see citing works
Fast Detection of Unsolvable Planning Instances Using Local Consistency
Bäckström, C., Jönsson, P., & Ståhlberg, S. (2021) · Proceedings of the International Symposium on Combinatorial Search · doi:10.1609/socs.v4i1.18294 · Cited by 32 · see citing works
Where 'Ignoring Delete Lists' Works: Local Search Topology in Planning Benchmarks
Hoffmann, J. (2005) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1747 · Cited by 32 · see citing works
A probabilistic analysis of prepositional STRIPS planning
Bylander, T. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(95)00055-0 · Cited by 28 · see citing works
Accuracy of admissible heuristic functions in selected planning domains
Helmert, M., & Mattmüller, R. (2008) · Cited by 27 · see citing works
Evaluating temporal planning domains
Cushing, W., Subbarao Kambhampati, B., Talamadupula, K., & Weld, D. S. (2007) · Cited by 24 · see citing works
Exploiting the Computational Power of the Graphics Card: Optimal State Space Planning on the GPU
Sulewski, D., Edelkamp, S., & Kissmann, P. (2011) · Proceedings of the International Conference on Automated Planning and Scheduling · doi:10.1609/icaps.v21i1.13464 · Cited by 20 · see citing works
The Answer Set Programming Competition
Calimeri, F., Ianni, G., Krennwallner, T., & Ricca, F. (2012) · AI Magazine · doi:10.1609/aimag.v33i4.2448 · Cited by 19 · see citing works
On the Hardness of Decision and Optimisation Problems
Slaney, J., & Thiébaux, S. (1998) · ANU Open Research (Australian National University) · Cited by 17 · see citing works
Phase transitions in classical planning: an experimental study
Rintanen, J. (2004) · Cited by 16 · see citing works
Automating the evaluation of planning systems
López, C. L., Jiménez, S., & Helmert, M. (2013) · AI Communications · doi:10.3233/aic-130572 · Cited by 8 · see citing works
On the Discovery and Utility of Precedence Constraints in Temporal Planning
Hu, Y., Yin, M., & Cai, D. (2011) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v25i1.8052 · Cited by 1 · see citing works
Flagged first, then by citations
3 of the 51 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- B.R. Kavuluri, Required concurrency without a scheduler, in: Proceedings of the Twenty-Eighth Workshop of the UK Special Interest Group on Planning and Scheduling, PlanSig-10, Brescia, Italy, 2010, pp. 71-78.
- J. Hoffmann, A tough nuts track for the IPC, in: The ICAPS-07 Workshop International Planning Competition: Past, Present and Future, Providence (Rhode Island), United States, 2007.
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- Authors
- Linares López, C., Jiménez Celorrio, S., & García Olaya, Á.
- Venue
- Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2015-06
- Volume
- 223
- Pages
- 82-119
- ISSN
- 0004-3702 (ISSN-L)
- OpenAlex
- W2042590837
- Cited by
- 71
- References
- 51
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 6 · 2023: 31 · 2022: 3 · 2021: 5 · 2020: 3 · 2019: 5 · 2018: 3 · 2017: 5 · 2016: 3 · 2015: 3 · 2014: 1
- FWCI
- 4.58
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Artificial Intelligence (publishedVersion, public-domain)
- Funders
- Decide Soluciones
- iActive
- University Carlos III de Madrid
- ICAPS
- Spanish Science Ministry (TIN2008-06701-C03-03)
- MINECO (TIN2011-27652-C03-02)
- 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 › Multi-Agent Systems and Negotiation
- Keywords
- International Planning Competition, deterministic planning, planning and scheduling, concurrency
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