{"type":"ScholarlyArticle","title":"Interval Selection with Binary Predictions","authors":[{"name":"Christodoulos Karavasilis","slug":"christodoulos-karavasilis","given":"Christodoulos","family":"Karavasilis","affiliation":""}],"abstract":"Following a line of work that takes advantage of vast machine-learned data to enhance online algorithms with (possibly erroneous) information about future inputs, we consider predictions in the context of deterministic algorithms for the problem of selecting a maximum weight independent set of intervals arriving on the real line. We look at two weight functions, unit (constant) weights, and weights proportional to the interval’s length. In the classical online model of irrevocable decisions, no algorithm can achieve constant competitiveness. In this setting, we show that a simple algorithm that is faithful to the predictions is optimal, and achieves an objective value of at least OPT - η, with η  being the total error in the predictions, both for unit, and proportional weights.\r\nWhen revocable acceptances (a form of preemption) are allowed, the optimal deterministic algorithm for unit weights is 2k-competitive, where k is the number of different interval lengths. We give an algorithm with performance OPT − η (and therefore 1-consistent), that is also (2k + 1)-robust. For proportional weights, there is an optimal (2φ + 1)-competitive algorithm, where φ  is the golden ratio. We present an algorithm with parameter λ > 1 that is 3λ / (λ  - 1) -consistent, and (4λ^2 + 2λ) / (λ - 1)-robust. Although these bounds are not tight, we show that for λ  > 3.42 we achieve consistency better than the optimal online guarantee, while maintaining bounded robustness.\r\nWe conclude with some experimental results on real-world data that complement our theoretical findings, and show the benefit of prediction algorithms for online interval selection, even in the presence of high error.","doi":"10.24963/ijcai.2025/948","url":"https://ijcai.org/proceedings/2025/948","pdf":"https://ijcai.org/proceedings/2025/0948.pdf","markdown":"https://ijcai.org/proceedings/2025/948.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/948","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Planning and Scheduling","track":"Main Track","number":948,"pages":{"first":8527,"last":8535},"keywords":["Planning and Scheduling: PS: Planning under uncertainty","Planning and Scheduling: PS: Real-time planning","Planning and Scheduling: PS: Scheduling","Planning and Scheduling: PS: Planning with Incomplete Information"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}