Bilevel optimization by conditional Bayesian optimization

Loading...
Thumbnail Image
Files
LOD2023_Bilevel_AV_3857.pdf(558.08 KB)
Accepted version
Date
2023-09-22
Authors
Dogan, Vedat
Prestwich, Steven
Journal Title
Journal ISSN
Volume Title
Publisher
Published Version
Research Projects
Organizational Units
Journal Issue
Abstract
Explainable AI (XAI) can greatly enhance user trust and satisfaction in AI-assisted decision-making processes. Numerous explanation techniques (explainers) exist in the literature, and recent findings suggest that addressing multiple user needs requires employing a combination of these explainers. We refer to such combinations as explanation strategies. This paper introduces iSee - Intelligent Sharing of Explanation Experience, an interactive platform that facilitates the reuse of explanation strategies and promotes best practices in XAI by employing the Case-based Reasoning (CBR) paradigm. iSee uses an ontology-guided approach to effectively capture explanation requirements, while a behaviour tree-driven conversational chatbot captures user experiences of interacting with the explanations and provides feedback. In a case study, we illustrate the iSee CBR system capabilities by formalising a real-world radiograph fracture detection system and demonstrating how each interactive tools facilitate the CBR processes.
Description
Keywords
Bilevel optimization , Conditional Bayesian optimization , Stackelberg games , Gaussian process
Citation
Dogan, V. and Prestwich, S. (2023) 'Bilevel optimization by conditional Bayesian optimization', The 9th International Conference on Machine Learning, Optimization, and Data Science, Grasmere, UK. September 22-26, forthcoming publication
Copyright
© the authors 2023