A Relevance-Based Compilation Method for Conformant Probabilistic Planning
Authors: Ran Taig, Ronen Brafman
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluation shows that this approach dominates existing state-of-the-art planners on almost all problem instances." and "Empirical Evaluation We implemented the algorithms and experimented with them on a variety of domains taken from PFF repository and the 2006 IPPC, as well as new, modified versions of these domains. ... Results are presented in Table 1. |
| Researcher Affiliation | Academia | Ran Taig and Ronen I. Brafman Computer Science Department Ben Gurion University of The Negev Beer-Sheva, Israel 84105 taig,brafman@cs.bgu.ac.il |
| Pseudocode | Yes | Algorithm 1 RBPP (P, conf-planner)" and "Algorithm 2 RESTRICT (P)" and "Algorithm 3 SORT-CLAUSES (P)" and "Algorithm 4 RESTRICT-CLAUSE (C, ψI, P) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it explicitly state that it is open-source or available for download. |
| Open Datasets | Yes | We implemented the algorithms and experimented with them on a variety of domains taken from PFF repository and the 2006 IPPC, as well as new, modified versions of these domains. |
| Dataset Splits | No | The paper references various domains and benchmarks (e.g., PFF repository, 2006 IPPC) but does not provide specific details on how data was split into training, validation, or test sets for its experiments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory, or cloud resources) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like 'PG s cf2cs code', 'NORSYS NETICA java api program', 'T-0', 'CFF', and 'GC[LAMA]' but does not provide specific version numbers for these ancillary software dependencies. |
| Experiment Setup | No | The paper describes algorithmic strategies and criteria for selecting and ordering clauses and propositions, but it does not provide specific numerical hyperparameters (e.g., learning rate, batch size, epochs) or detailed system-level training configurations for experimental setup. |