Structural Symmetries for Fully Observable Nondeterministic Planning
Authors: Dominik Winterer, Martin Wehrle, Michael Katz
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have implemented the variant of LAO which has been previously used for FOND planning [Mattm uller et al., 2010] as well as CLAO within the Fast Downward planning system [Helmert, 2006]. In this section, we empirically investigate the usefulness of structural symmetries and symmetry reduction for FOND planning. In particular, we investigate the following research questions: In how many of the commonly available FOND planning domains do symmetries exist? What is the computational overhead to compute these symmetries? How much can the state space size be reduced with CLAO compared to LAO ? And finally, does the reduction pay off in overall runtime and in the number of tasks that can be solved? As a basis for these investigations, we compare the performance of our LAO implementation to the LAO -based FOND planner my ND. |
| Researcher Affiliation | Academia | Dominik Winterer University of Freiburg Germany wintered@cs.uni-freiburg.de Martin Wehrle University of Basel Switzerland martin.wehrle@unibas.ch Michael Katz IBM Watson Health Israel katzm@il.ibm.com |
| Pseudocode | Yes | Algorithm 1 The CLAO algorithm; Algorithm 2 Solution reconstruction algorithm |
| Open Source Code | No | The paper states, "We have implemented our framework within the Fast Downward planning system [Helmert, 2006]" but does not provide a direct link or explicit statement about making their specific implementation code open-source or publicly available. |
| Open Datasets | Yes | Our benchmark set consists of all IPC-08 FOND planning domains, including the larger tasks generated by Muise et al. [2012]. |
| Dataset Splits | No | The paper uses standard planning benchmarks but does not provide specific details on how the datasets were split into training, validation, or test sets (e.g., percentages or sample counts). |
| Hardware Specification | Yes | Our experiments have been conducted on a server equipped with 2.3 GHz Quad-Core AMD Opteron 2356 CPUs. |
| Software Dependencies | No | The paper mentions "Fast Downward planning system [Helmert, 2006]", "FF heuristic [Hoffmann and Nebel, 2001]", and "my ND planner [Mattm uller et al., 2010]", but it does not provide specific version numbers for these software dependencies. |
| Experiment Setup | No | The paper states it uses the FF heuristic and notes it's computed based on alloutcomes determinizations, but it does not provide specific hyperparameters, training configurations, or detailed system-level settings for the experiments. |