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 * TODO: Jörg mentioned some security tool that uses FF and LAMA as "automated hacking tools"
 for finding attack vectors. More details needed.		  * TODO: Jörg mentioned some security tool that uses FF and LAMA as "automated hacking tools" for finding attack vectors. More details needed.
Line 13: Line 12:
 . Jörg Hoffmann, Nazim Fatès and Héctor Palacios encode the problem of finding fixed point configurations for
 certain asynchronously updating cellular automata as a classical planning problem. They use different planning
 systems, including LAMA, to find such fixed points. In their experiments, LAMA performs better than FF but worse
 than SATPLAN, indicating that this a domain where current heuristic search approaches fare atypically badly.
 . ''Reference:'' Hoffmann, J., Fatès, N., & Palacios, H. (2010). Brothers in Arms? On AI Planning and Cellular
 Automata. In ''Proc. ECAI 2010'', pp. 223-228.		  . Jörg Hoffmann, Nazim Fatès and Héctor Palacios encode the problem of finding fixed point configurations for certain asynchronously updating cellular automata as a classical planning problem. They use different planning systems, including LAMA, to find such fixed points. In their experiments, LAMA performs better than FF but worse than SATPLAN, indicating that this a domain where current heuristic search approaches fare atypically badly.
 . ''Reference:'' Hoffmann, J., Fatès, N., & Palacios, H. (2010). Brothers in Arms? On AI Planning and Cellular Automata. In ''Proc. ECAI 2010'', pp. 223-228.
Line 21: Line 16:
 . Alexander Koller and Jörg Hoffmann describe how to express the problem of natural-language sentence generation
 as a classical planning task and use FF, LAMA and a modified version of FF to solve the resulting planning tasks.
 In their experiments, LAMA outperforms the unmodified version of FF by several orders of magnitude, but is in turn
 outperformed by the modified version of FF by several orders of magnitude.
 . ''Reference:'' Koller, A., & Hoffmann, J. (2010). Waking Up a Sleeping Rabbit: On Natural-Language Sentence
 Generation with FF. In ''Proc. ICAPS 2010'', pp. 238-241.		  . Alexander Koller and Jörg Hoffmann describe how to express the problem of natural-language sentence generation as a classical planning task and use FF, LAMA and a modified version of FF to solve the resulting planning tasks. In their experiments, LAMA outperforms the unmodified version of FF by several orders of magnitude, but is in turn outperformed by the modified version of FF by several orders of magnitude.
 . ''Reference:'' Koller, A., & Hoffmann, J. (2010). Waking Up a Sleeping Rabbit: On Natural-Language Sentence Generation with FF. In ''Proc. ICAPS 2010'', pp. 238-241.
Line 29: Line 20:
 . Emil Keyder and Héctor Geffner describe a technique for compiling away soft goals (á la the IPC-2008
 net benefit track) and apply LAMA to the resulting problems. The LAMA-based planner performed drastically
 better than native over-subscription planners.		  . Emil Keyder and Héctor Geffner describe a technique for compiling away soft goals (à la the IPC-2008 net benefit track) and apply LAMA to the resulting problems. The LAMA-based planner performed drastically better than native over-subscription planners.
Line 35: Line 24:
 . Blai Bonet, Héctor Palacios and Héctor Geffner show how to solve control problems by compiling
 the problem of generating a memoryless or finite-state controller into a classical planning task.
 These classical planning tasks are then solved with LAMA or SATPLAN. (In their experiments, neither
 planner dominates the other.)
 . ''Reference:'' Bonet, B., Palacios, H., & Geffner, H. (2009). Automatic Derivation of Memoryless Policies
 and Finite-State Controllers Using Classical Planners. In ''Proc. ICAPS 2009'', pp. 34-41.		  . Blai Bonet, Héctor Palacios and Héctor Geffner show how to solve control problems by compiling the problem of generating a memoryless or finite-state controller into a classical planning task. These classical planning tasks are then solved with LAMA or SATPLAN. (In their experiments, neither planner dominates the other.)
 . ''Reference:'' Bonet, B., Palacios, H., & Geffner, H. (2009). Automatic Derivation of Memoryless Policies and Finite-State Controllers Using Classical Planners. In ''Proc. ICAPS 2009'', pp. 34-41.
Line 43: Line 28:
 . Akihiro Kishimoto, Alex Fukunaga and Adi Botea have parallelized Fast Downward and run the
 distributed version on a !SunFire X4600 cluster consisting of 8 machines with a total of 64 CPUs,
 128 CPU cores and 256 GB RAM. They typically achieve a search speedup of 30-60 compared to a sequential
 version of the planner.
 . ''Reference:'' Kishimoto, A., Fukunaga, A., & Botea, A. (2009). Scalable, Parallel Best-First Search for Optimal
 Sequential Planning. In ''Proc. ICAPS 2009'', pp. 201-208.
 		  . Akihiro Kishimoto, Alex Fukunaga and Adi Botea have parallelized Fast Downward and run the distributed version on a !SunFire X4600 cluster consisting of 8 machines with a total of 64 CPUs, 128 CPU cores and 256 GB RAM. They typically achieve a search speedup of 30-60 compared to a sequential version of the planner.
 . ''Reference:'' Kishimoto, A., Fukunaga, A., & Botea, A. (2009). Scalable, Parallel Best-First Search for Optimal Sequential Planning. In ''Proc. ICAPS 2009'', pp. 201-208.

Back to the HomePage.

Who uses Fast Downward?

The following list shows some of the uses that people have found for the Fast Downward and LAMA planners.

/!\ We have only just started collecting this list, so as of now it is very incomplete.

  • TODO: Jörg mentioned some security tool that uses FF and LAMA as "automated hacking tools" for finding attack vectors. More details needed.

  • proving properties of cellular automata: [2010]

  • Jörg Hoffmann, Nazim Fatès and Héctor Palacios encode the problem of finding fixed point configurations for certain asynchronously updating cellular automata as a classical planning problem. They use different planning systems, including LAMA, to find such fixed points. In their experiments, LAMA performs better than FF but worse than SATPLAN, indicating that this a domain where current heuristic search approaches fare atypically badly.

  • Reference: Hoffmann, J., Fatès, N., & Palacios, H. (2010). Brothers in Arms? On AI Planning and Cellular Automata. In Proc. ECAI 2010, pp. 223-228.

  • natural language sentence generation [2010]

  • Alexander Koller and Jörg Hoffmann describe how to express the problem of natural-language sentence generation as a classical planning task and use FF, LAMA and a modified version of FF to solve the resulting planning tasks. In their experiments, LAMA outperforms the unmodified version of FF by several orders of magnitude, but is in turn outperformed by the modified version of FF by several orders of magnitude.

  • Reference: Koller, A., & Hoffmann, J. (2010). Waking Up a Sleeping Rabbit: On Natural-Language Sentence Generation with FF. In Proc. ICAPS 2010, pp. 238-241.

  • over-subscription planning (planning with soft goals) [2009]

  • Emil Keyder and Héctor Geffner describe a technique for compiling away soft goals (à la the IPC-2008 net benefit track) and apply LAMA to the resulting problems. The LAMA-based planner performed drastically better than native over-subscription planners.

  • Reference: Keyder, E., & Geffner, H. (2009). Soft Goals Can Be Compiled Away. JAIR, 36, 547-556

  • finite-state controller synthesis [2009]

  • Blai Bonet, Héctor Palacios and Héctor Geffner show how to solve control problems by compiling the problem of generating a memoryless or finite-state controller into a classical planning task. These classical planning tasks are then solved with LAMA or SATPLAN. (In their experiments, neither planner dominates the other.)

  • Reference: Bonet, B., Palacios, H., & Geffner, H. (2009). Automatic Derivation of Memoryless Policies and Finite-State Controllers Using Classical Planners. In Proc. ICAPS 2009, pp. 34-41.

  • massively parallel planning [2000]

  • Akihiro Kishimoto, Alex Fukunaga and Adi Botea have parallelized Fast Downward and run the distributed version on a SunFire X4600 cluster consisting of 8 machines with a total of 64 CPUs, 128 CPU cores and 256 GB RAM. They typically achieve a search speedup of 30-60 compared to a sequential version of the planner.

  • Reference: Kishimoto, A., Fukunaga, A., & Botea, A. (2009). Scalable, Parallel Best-First Search for Optimal Sequential Planning. In Proc. ICAPS 2009, pp. 201-208.