Structured Possibilistic Planning Using Decision Diagrams
Authors: Nicolas Drougard, Florent Teichteil-Königsbuch, Jean-Loup Farges, Didier Dubois
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that PPUDD s computation time is much lower than SPUDD, Symbolic-HSVI and APPL for possibilistic and probabilistic versions of the same benchmarks under either total or mixed observability, while still providing high-quality policies. |
| Researcher Affiliation | Collaboration | Nicolas Drougard, Florent Teichteil-K onigsbuch Jean-Loup Farges Onera The French Aerospace Lab 2 avenue Edouard Belin 31055 Toulouse Cedex 4, France Didier Dubois IRIT Paul Sabatier University 118 route de Narbonne 31062 Toulouse Cedex 4, France |
| Pseudocode | Yes | Algorithm 1: PPUDD |
| Open Source Code | No | The paper does not include any statement or link regarding the public availability of the source code for the described methodology. |
| Open Datasets | Yes | navigation domain used in planning competitions (Sanner 2011) ... Rocksample problem (RS) against a recent probabilistic MOMDP planner, APPL (Ong et al. 2010), and a POMDP planner using ADDs, symbolic HSVI (Sim et al. 2008). |
| Dataset Splits | No | The paper describes using specific benchmarks (navigation domain, Rocksample problem) but does not provide explicit details about train/validation/test dataset splits (e.g., percentages, sample counts, or specific split files). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9) or mention specific solver versions. |
| Experiment Setup | Yes | In this domain, a robot navigates in a grid where it must reach some goal location most reliably. It can apply actions going north, east, south, west and stay which all cost 1 except on the goal... This probabilistic model is approximated by two possibilistic ones where: the preference of reaching the goal is 1; in the first model (M1) the highest probability of each Bernoulli distribution is replaced by 1 (for possibility normalization reasons) and the same value for the lowest probability is kept; for the second model (M2), the probability of disappearing is replaced by 1 and the other one is kept. ... Both algorithms are approximate and anytime, so we decided to stop them when they reach a precision of 1. |