{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/synthetic-ground-truth-generation-for","title":"Synthetic Ground Truth Generation for Evaluating Generative Policy Models","arxiv_id":"1904.13233","date":"2019-04-26","proceeding":null,"authors":["Daniel Cunnington","Graham White","Geeth de Mel"],"abstract":"Generative Policy-based Models aim to enable a coalition of systems, be they\ndevices or services to adapt according to contextual changes such as\nenvironmental factors, user preferences and different tasks whilst adhering to\nvarious constraints and regulations as directed by a managing party or the\ncollective vision of the coalition. Recent developments have proposed new\narchitectures to realize the potential of GPMs but as the complexity of systems\nand their associated requirements increases, there is an emerging requirement\nto have scenarios and associated datasets to realistically evaluate GPMs with\nrespect to the properties of the operating environment, be it the future\nbattlespace or an autonomous organization. In order to address this\nrequirement, in this paper, we present a method of applying an agile knowledge\nrepresentation framework to model requirements, both individualistic and\ncollective that enables synthetic generation of ground truth data such that\nadvanced GPMs can be evaluated robustly in complex environments. We also\nrelease conceptual models, annotated datasets, as well as means to extend the\ndata generation approach so that similar datasets can be developed for varying\ncomplexities and different situations.","url_abs":"http://arxiv.org/abs/1904.13233v1","url_pdf":"http://arxiv.org/pdf/1904.13233v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"synthetic-ground-truth-generation-for","repo_url":"https://github.com/dais-ita/coalition-data","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}