Qualitative Spatial Logics for Buffered Geometries
Authors: Heshan Du, Natasha Alechina
JAIR 2016 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we briefly describe how the logics are used in a system for generating and debugging matches between spatial objects, and report positive experimental evaluation results for the system. |
| Researcher Affiliation | Academia | Heshan Du EMAIL University of Leeds, UK Natasha Alechina EMAIL University of Nottingham, UK |
| Pseudocode | No | The paper describes the logic, axioms, and inference rules, and how a reasoner based on these axioms was implemented. However, it does not include any explicitly labeled pseudocode or algorithm blocks. The rules are logical statements, not algorithmic steps. |
| Open Source Code | No | The paper mentions that "A dedicated LBPT reasoner integrated with an assumption-based truth maintenance system (ATMS) was developed as a part of Match Maps." While it refers to a system, it does not provide any explicit statement about the source code being open-source or offer a link to a code repository. |
| Open Datasets | Yes | In experiments, the LBPT reasoner with an ATMS was used to validate matches between spatial objects from OSM data (building layer) and OSGB Master Map data (Address Layer and Topology Layer) (Du et al., 2015). The study areas are in city centres of Nottingham UK and Southampton UK. The Nottingham data was obtained in 2012, and the Southampton data in 2013. Journal of Artificial Intelligence Research 56 (2016) 693-745 Submitted 03/16; published 08/16 Open Street Map (2012). The Free Wiki World Map. http://www.openstreetmap.org. Ordnance Survey (2012). Ordnance Survey. http://www.ordnancesurvey.co.uk. |
| Dataset Splits | No | The paper provides details on the number of spatial objects used from OSM and OSGB data for Nottingham and Southampton in Table 1. However, it does not specify any training/test/validation splits for these datasets in the context of machine learning experiments, but rather describes how the datasets were used for match validation. |
| Hardware Specification | No | The paper discusses experimental evaluation results and the performance of the LBPT reasoner, but it does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used for running these experiments. |
| Software Dependencies | No | The paper states that "A dedicated LBPT reasoner integrated with an assumption-based truth maintenance system (ATMS) was developed as a part of Match Maps." However, it does not provide specific version numbers for this reasoner, ATMS, or any other software dependencies. |
| Experiment Setup | No | The paper describes the overall approach of validating matches using LBPT reasoning within the Match Maps system and some details about how matches are generated. However, it does not specify concrete experimental setup details such as hyperparameter values, optimizer settings, or other system-level training configurations typically found in experimental setups. |