Plan Recognition in Continuous Domains
Authors: Gal Kaminka, Mor Vered, Noa Agmon
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide formal arguments for the usefulness of mirroring, and empirically evaluate mirroring in more than a thousand recognition problems in three continuous domains and six classical planning domains. |
| Researcher Affiliation | Academia | Gal A. Kaminka, Mor Vered, Noa Agmon Computer Science Department Bar Ilan University, Israel {galk,veredm,agmon}@cs.biu.ac.il |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It mentions using open-source libraries and simulators like OMPL, ROS, and Gazebo, but not its own implementation code. |
| Open Datasets | Yes | We used the Open Motion Planning Library (OMPL (S ucan, Moll, and Kavraki 2012)) cubicles environment along with the default robot... We used the entire set of benchmark plan-recognition problems used in (Ram ırez and Geffner 2010) and then in (Sohrabi, Riabov, and Udrea 2016; Pereira, Oren, and Meneguzzi 2016)... We additionally evaluated our continuous goal recognizer on the shape sketch recognition domain introduced in (Vered, Kaminka, and Biham 2016). |
| Dataset Splits | No | The paper does not explicitly state training, validation, or test dataset splits. It describes generating recognition problems or using existing benchmarks, but not the specific partitioning of data into these subsets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running its experiments. It mentions a 'robot' and 'simulator' but no hardware specifications. |
| Software Dependencies | No | The paper mentions software like OMPL, ROS, Gazebo, and the hspf planner, but it does not specify version numbers for these or any other ancillary software components. |
| Experiment Setup | Yes | Each call to the planner was given a time limit of 1 sec. For cost, we measured the length of the path. We set 11 points spread through the cubicles environments. We then generated two observed paths from each point to all others, for a total of 220 = 110 * 2 recognition problems. Each plan consisting of between 20-75 points. The observations were obtained by running the asymptotically optimal planner RRT* on each pair of points (time limit of 5 minutes). |