{"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/machine-learning-meets-quantitative-planning","title":"Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Autonomous Robots","arxiv_id":"1903.03920","date":"2019-03-10","proceeding":null,"authors":["Pooyan Jamshidi","Javier Cámara","Bradley Schmerl","Christian Kästner","David Garlan"],"abstract":"Modern cyber-physical systems (e.g., robotics systems) are typically composed\nof physical and software components, the characteristics of which are likely to\nchange over time. Assumptions about parts of the system made at design time may\nnot hold at run time, especially when a system is deployed for long periods\n(e.g., over decades). Self-adaptation is designed to find reconfigurations of\nsystems to handle such run-time inconsistencies. Planners can be used to find\nand enact optimal reconfigurations in such an evolving context. However, for\nsystems that are highly configurable, such planning becomes intractable due to\nthe size of the adaptation space. To overcome this challenge, in this paper we\nexplore an approach that (a) uses machine learning to find Pareto-optimal\nconfigurations without needing to explore every configuration and (b) restricts\nthe search space to such configurations to make planning tractable. We explore\nthis in the context of robot missions that need to consider task timeliness and\nenergy consumption. An independent evaluation shows that our approach results\nin high-quality adaptation plans in uncertain and adversarial environments.","url_abs":"http://arxiv.org/abs/1903.03920v1","url_pdf":"http://arxiv.org/pdf/1903.03920v1.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":"machine-learning-meets-quantitative-planning","repo_url":"https://github.com/cmu-mars/model-learner","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}