Hostname: page-component-84b7d79bbc-tsvsl Total loading time: 0 Render date: 2024-07-26T00:57:20.101Z Has data issue: false hasContentIssue false
  • English
  • Français

Parallel heuristic search in forward partial-order planning

Published online by Cambridge University Press:  20 December 2016

Oscar Sapena Open the ORCID record for Oscar Sapena [Opens in a new window] ,
Alejandro Torreño Open the ORCID record for Alejandro Torreño [Opens in a new window]  and
Eva Onaindía Open the ORCID record for Eva Onaindía [Opens in a new window]
Oscar Sapena
Affiliation:
Department of Information Systems and Computation, Universitat Politècnica de València, Camino de Vera, s/n, 46022València, Spain e-mail: osapena@dsic.upv.es, atorreno@dsic.upv.es, onaindia@dsic.upv.es
Alejandro Torreño
Affiliation:
Department of Information Systems and Computation, Universitat Politècnica de València, Camino de Vera, s/n, 46022València, Spain e-mail: osapena@dsic.upv.es, atorreno@dsic.upv.es, onaindia@dsic.upv.es
Eva Onaindía
Affiliation:
Department of Information Systems and Computation, Universitat Politècnica de València, Camino de Vera, s/n, 46022València, Spain e-mail: osapena@dsic.upv.es, atorreno@dsic.upv.es, onaindia@dsic.upv.es
Get access Rights & Permissions [Opens in a new window]

Abstract

Most of the current top-performing planners are sequential planners that only handle total-order plans. Although this is a computationally efficient approach, the management of total-order plans restrict the choices of reasoning and thus the generation of flexible plans. In this paper, we present FLAP2, a forward-chaining planner that follows the principles of the classical POCL (Partial-Order Causal-Link Planning) paradigm. Working with partial-order plans allows FLAP2 to easily manage the parallelism of the plans, which brings several advantages: more flexible executions, shorter plan durations (makespan) and an easy adaptation to support new features like temporal or multi-agent planning. However, one of the limitations of POCL planners is that they require far more computational effort to deal with the interactions that arise among actions. FLAP2 minimizes this overhead by applying several techniques that improve its performance: the combination of different state-based heuristics and the use of parallel processes to diversify the search in different directions when a plateau is found. To evaluate the performance of FLAP2, we have made a comparison with four state-of-the-art planners: SGPlan, YAHSP2, Temporal Fast Downward and OPTIC. Experimental results show that FLAP2 presents a very acceptable trade-off between time and quality and a high coverage on the current planning benchmarks.

Type
Articles
Information
The Knowledge Engineering Review , Volume 31 , Special Issue 5: Constraint Satisfaction for Planning and Scheduling , November 2016 , pp. 417 - 428
Copyright
© Cambridge University Press, 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Benton, J., Coles, A. & Coles, A. 2012. Temporal planning with preferences and time-dependent continuous costs. In International Conference on Automated Planning and Scheduling (ICAPS), 2–10.Google Scholar
Chen, Y., Wah, B. & Hsu, C. 2006. Temporal planning using subgoal partitioning and resolution in SGPlan. Journal of Artificial Intelligence Research 26, 323–369.Google Scholar
Coles, A., Coles, A., Fox, M. & Long, D. 2010. Forward-chaining partial-order planning. In International Conference on Automated Planning and Scheduling (ICAPS), 42–49.Google Scholar
Domshlak, C., Karpas, E. & Markovitch, S. 2010. To max or not to max: online learning for speeding up optimal planning. In AAAI Conference on Artificial Intelligence.Google Scholar
Eyerich, P. 2012. Preferring properly: increasing coverage while maintaining quality in anytime temporal planning. In Proceedings of the European Conference on Artificial Intelligence (ECAI), 312–317.Google Scholar
Eyerich, P., Mattmüller, R. & Röger, G. 2009. Using the context-enhanced additive heuristic for temporal and numeric planning. In Proceedings of the 19th International Conference on Automated Planning and Scheduling (ICAPS).Google Scholar
Helmert, M. 2004. A planning heuristic based on causal graph analysis. In International Conference on Automated Planning and Scheduling (ICAPS), 161–170.Google Scholar
Helmert, M. 2006. The fast downward planning system. Journal of Articial Intelligence Research 26, 191–246.Google Scholar
Helmert, M., Haslum, P., Hoffmann, J. & Nissim, R. 2013. Merge & shrink abstraction: a method for generating lower bounds in factored state spaces. Journal of the ACM 61, 1–16:63.Google Scholar
Helmert, M., Röger, G. & Karpas, E. 2011. Fast downward stone soup: a baseline for building planner portfolios. In Proceedings of the ICAPS-2011 Workshop on Planning and Learning (PAL), 28–35.Google Scholar
Hoffmann, J. 2002. Extending FF to numerical state variables. In Proceedings of the 15th European Conference on Artificial Intelligence (ECAI), 571–575.Google Scholar
Hoffman, J. & Nebel, B. 2001. The FF planning system: fast planning generation through heuristic search. Journal of Artificial Intelligence Research 14, 253–302.Google Scholar
Hoffmann, J., Porteous, J. & Sebastia, L. 2004. Ordered landmarks in planning. Journal of Artificial Intelligence Research 22, 215–278.Google Scholar
Keyder, E. & Geffner, H. 2008. Heuristics for planning with action costs revisited. In European Conference on Artificial Intelligence (ECAI), 588–592.Google Scholar
Khouadjia, M., Schoenauer, M., Vidal, V., Dréo, J. & Savéant, P. 2013. Multi-objective AI planning: comparing aggregation and pareto approaches. In Proceedings of the 13th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP), 7832: 202–213.Google Scholar
Kvarnström, J. 2011. Planning for loosely coupled agents using partial order forward-chaining. International Conference on Automated Planning and Scheduling (ICAPS), 138–145.Google Scholar
Lipovetzky, N. & Geffner, H. 2009. Inference and decomposition in planning using causal consistent chains. Proceedings of the Nineteenth International Conference on Automated Planning and Scheduling (ICAPS), 217–224.Google Scholar
Richter, S. & Westphal, M. 2010. The LAMA planner: guiding cost-based anytime planning with landmarks. Journal of Artificial Intelligence Research 29(1), 127–177.Google Scholar
Russell, S. J. & Norvig, P. 2009. Artificial Intelligence: A Modern Approach, 3rd edition. Prentice Hall.Google Scholar
Sapena, O., Onaindía, E. & Torreño, A. 2015. FLAP: applying least-commitment in forward-chaining planning. AI Communications 28(1), 5–20.Google Scholar
Sebastia, L., Onaindía, E. & Marzal, E. 2006. Decomposition of planning problems. AI Communications 19(1), 49–81.Google Scholar
Torreño, A., Onaindía, E. & Sapena, O. 2014. FMAP: distributed cooperative multi-agent planning. Applied Intelligence 41(2), 606–626.Google Scholar
Vidal, V. 2003. A lookahead strategy for solving large planning problems. In Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI), 1524–1525.Google Scholar
Vidal, V. 2011. YAHSP2: keep it simple, stupid. In Proceedings of the 7th International Planning Competition (IPC-2011), 83–90.Google Scholar