{"type":"ScholarlyArticle","title":"Faster Conflict Generation for Dynamic Controllability","authors":[{"name":"Nikhil Bhargava","slug":"nikhil-bhargava","given":"Nikhil","family":"Bhargava","affiliation":"Massachusetts Institute of Technology"},{"name":"Tiago Vaquero","slug":"tiago-vaquero","given":"Tiago","family":"Vaquero","affiliation":"Massachusetts Institute of Technology"},{"name":"Brian Williams","slug":"brian-williams","given":"Brian","family":"Williams","affiliation":"Massachusetts Institute of Technology"}],"abstract":"In this paper, we focus on speeding up the temporal plan relaxation problem for dynamically controllable systems. We take a look at the current best-known algorithm for determining dynamic controllability and augment it to efficiently generate conflicts when the network is deemed uncontrollable. Our work preserves the O(n^3) runtime of the best available dynamic controllability checker and improves on the previous best runtime of O(n^4) for extracting dynamic controllability conflicts. We then turn our attention to temporal plan relaxation tasks and show how we can leverage our work on conflicts and the structure of the network to efficiently make incremental updates intended to restore dynamic controllability by relaxing constraints. Our new algorithm, RelaxIDC, has the same asymptotic runtime as previous algorithms but sees dramatic empirical improvements over the course of repeated dynamic controllability checks.","doi":"10.24963/ijcai.2017/598","url":"https://ijcai.org/proceedings/2017/598","pdf":"https://ijcai.org/proceedings/2017/0598.pdf","markdown":"https://ijcai.org/proceedings/2017/598.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/598","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Planning and Scheduling","track":"Main track","number":598,"pages":{"first":4280,"last":4286},"keywords":["Planning and Scheduling: Search in Planning and Scheduling","Planning and Scheduling: Scheduling","Uncertainty in AI: Uncertainty in AI"],"videos":[],"references":[{"position":1,"raw":"[Fang et al., 2014] Cheng Fang, Peng Yu, and Brian C Williams. 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