Privacy Preserving Plans in Partially Observable Environments
Authors: Sarah Keren, Avigdor Gal, Erez Karpas
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluation shows the feasibility of the proposed solution. and Empirical evaluation is presented in Section 5 |
| Researcher Affiliation | Academia | Technion Israel Institute of Technology and {sarahn@tx,avigal@ie,karpase@}.technion.ac.il |
| Pseudocode | Yes | Figure 2: The common-declare compilation which describes the planning problem P' and its components in a structured, definitional format, including actions and costs. |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about the availability of open-source code for the described methodology. |
| Open Datasets | Yes | We used 20 problems from the LOGISTICS (LOG) domain and 12 from the BLOCKSWORLD domain (BLOCK), which were shown to be the most computationally challenging for goal recognition design in previous work [Keren et al., 2016] and 34 GRIDNAVIGATION (GRID) problems of size 25 25. |
| Dataset Splits | No | The paper does not provide specific dataset split information (like train/validation/test percentages or sample counts). It refers to sets of 'problems' from planning domains which are evaluated, but not split. |
| Hardware Specification | Yes | Experiments were run on Intel(R) Xeon(R) CPU X5690 machines, with a time limit of 30 minutes and memory limit of 2 GB. |
| Software Dependencies | No | The paper mentions using 'the Fast Downward planning system [Helmert, 2006] running A with the LM-CUT heuristic [Helmert and Domshlak, 2009]' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | For each problem, we created 5 versions: Fully observable (FULL), Non observable actions (NO), Partially observable deterministic (POD), Partially observable non-deterministic (POND)... We compared three compilations: latest Split (LS) [Keren et al., 2014], latest Expose (LE) [Keren et al., 2016], and common-declare (CD)... We used the Fast Downward planning system [Helmert, 2006] running A with the LM-CUT heuristic [Helmert and Domshlak, 2009]. |