{"type":"ScholarlyArticle","title":"Optimizing Interactive Systems with Data-Driven Objectives","authors":[{"name":"Ziming Li","slug":"ziming-li","given":"Ziming","family":"Li","affiliation":"University of Amsterdam , Netherlands"}],"abstract":"Effective optimization is essential for interactive systems to provide a satisfactory user experience. However, it is often challenging to find an objective to optimize for. Generally, such objectives are manually crafted and rarely capture complex user needs in an accurate manner. We propose to infer the objective directly from observed user interactions. These inferences can be made regardless of prior knowledge and across different types of user behavior. It is promising if we model the objectives directly from the user interactions which we use to optimize interactive systems, which will improve user experience and dynamically reacts to user actions.","doi":"10.24963/ijcai.2019/912","url":"https://ijcai.org/proceedings/2019/912","pdf":"https://ijcai.org/proceedings/2019/0912.pdf","markdown":"https://ijcai.org/proceedings/2019/912.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/912","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":912,"pages":{"first":6446,"last":6447},"keywords":["Machine Learning: Reinforcement Learning","Agent-based and Multi-agent Systems: Human-Agent Interaction","Machine Learning Applications: Applications of Reinforcement Learning"],"videos":[],"references":[{"position":1,"raw":"[Kosinski et al., 2013] Michal Kosinski, David Stillwell, and Thore Graepel. 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