An Effective Polynomial Technique for Compiling Conditional Effects Away
Authors: Alfonso Emilio Gerevini, Francesco Percassi, Enrico Scala
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental analysis indicates that this approach enables the effective use of polynomial compilations, offering benefits in terms of modularity and reusability of existing planners. It also demonstrates that a compilation-based approach can be more efficient, either independently or in synergy with state-of-the-art optimal planners that directly support conditional effects. |
| Researcher Affiliation | Academia | 1Dipartimento di Ingegneria dell Informazione, Universit a degli Studi di Brescia, Italy 2School of Computing and Engineering, University of Huddersfield, United Kingdom alfonso.gerevini@unibs.it,f.percassi@hud.ac.uk,enrico.scala@unibs.it |
| Pseudocode | Yes | Figure 1: Algorithm 1: Computing a size-approximated MFPS. |
| Open Source Code | Yes | We compared our compilation (COCOA) with those provided by Gazen and Knoblock (1997) (GKCOMP) and Nebel (2000). (Source code: https://gitlab.com/Edmond Dantes/cocoa2.0.) |
| Open Datasets | Yes | Benchmarks. We collected domains with CEs from different sources: Fast Downward benchmark collection (https:// gitlab.com/aibasel/downward-benchmarks), problems generated by conformant-to-classical planning compilations (Palacios and Geffner 2009; Grastien and Scala 2017). Additionally, we also included domains used in the work by R oger, Pommerening, and Helmert (2014). |
| Dataset Splits | No | The paper does not explicitly provide details about validation dataset splits. |
| Hardware Specification | Yes | The experiments were run on an Intel Xeon Gold 6140M CPU with 2.30 GHz. |
| Software Dependencies | No | All planners used in our experiments are based on the Fast Downward planning system (Helmert 2006). |
| Experiment Setup | Yes | We give a budget of 1800 seconds of runtime, 8 GB of memory for each run (compilation plus solving) and 2 GB of disk space. |