{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T06:57:11Z","timestamp":1781161031671,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":13,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819200672","type":"print"},{"value":"9789819200689","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-92-0068-9_13","type":"book-chapter","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T06:07:35Z","timestamp":1781158055000},"page":"184-199","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring Action Selection Methods in\u00a0User-Driven Image Generation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-2815-8948","authenticated-orcid":false,"given":"Daniil","family":"Hardzetski","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7606-3057","authenticated-orcid":false,"given":"Urszula","family":"Markowska-Kaczmar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,1]]},"reference":[{"key":"13_CR1","unstructured":"Hejna, J., et al.: Contrastive preference learning: learning from human feedback without reinforcement learning. In: The Twelfth International Conference on Learning Representations (2024). https:\/\/openreview.net\/forum?id=iX1RjVQODj"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Hejna, J., Sadigh, D.: Inverse preference learning: preference-based RL without a reward function. arXiv 2305.15363 (2023)","DOI":"10.52202\/075280-0825"},{"key":"13_CR3","unstructured":"Helbling, A., Rozell, C.J., O\u2019Shaughnessy, M., Fallah, K.: Prefgen: preference guided image generation with relative attributes. arXiv 2304.00185 (2023)"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Heng, W., F\u00fcrnkranz, J., H\u00fcllermeier, E., et\u00a0al: Preference-based policy iteration: leveraging preference learning for reinforcement learning. In: Machine Learning and Knowledge Discovery in Databases, pp. 312\u2013327. Springer (2011)","DOI":"10.1007\/978-3-642-23780-5_30"},{"key":"13_CR5","doi-asserted-by":"publisher","unstructured":"Kazemi, H., Taherkhani, F., Nasrabadi, N.M.: Preference-based image generation. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 3393\u20133402 (2020). https:\/\/doi.org\/10.1109\/WACV45572.2020.9093406","DOI":"10.1109\/WACV45572.2020.9093406"},{"key":"13_CR6","unstructured":"Knox, W.B., Hatgis-Kessell, S., Booth, S., Niekum, S., Stone, P., Allievi, A.: Models of human preference for learning reward functions. arXiv 2206.02231 (2023)"},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.425"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Marcin\u00f3w, A., et al: Building a dataset of wroc\u0142aw\u2019s historic tenements: Image annotation for machine learning applications. Architectus. (2024)","DOI":"10.37190\/arc240306"},{"key":"13_CR9","unstructured":"Novoseller, E.R., Wei, Y., Sui, Y., Yue, Y., Burdick, J.W.: Dueling posterior sampling for preference-based reinforcement learning. arXiv 1908.01289 (2020)"},{"key":"13_CR10","unstructured":"Wirth, C., Akrour, R., Neumann, G., F\u00fcrnkranz, J.: A survey of preference-based reinforcement learning methods. J. Mach. Learn. Res. 18(136), 1\u201346 (2017). http:\/\/jmlr.org\/papers\/v18\/16-634.html"},{"key":"13_CR11","unstructured":"Wirth, C., F\u00fcrnkranz, J.: EPMC: every visit preference monte carlo for reinforcement learning. In: Proceedings of the 5th Asian Conference on Machine Learning. Proceedings of Machine Learning Research, vol.\u00a029, pp. 483\u2013497. PMLR (2013). https:\/\/proceedings.mlr.press\/v29\/Wirth13.html"},{"key":"13_CR12","unstructured":"Xu, Y., Wang, R., Yang, L.F., Singh, A., Dubrawski, A.: Preference-based reinforcement learning with finite-time guarantees. arXiv 2006.08910 (2020)"},{"key":"13_CR13","unstructured":"Zhang, H., et al.: Stackgan++: realistic image synthesis with stacked generative adversarial networks. arXiv 1710.10916 (2018)"}],"container-title":["Communications in Computer and Information Science","Recent Challenges in Intelligent information and Database Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-0068-9_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T06:07:41Z","timestamp":1781158061000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-0068-9_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819200672","9789819200689"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-0068-9_13","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 June 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ACIIDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Intelligent Information and Database Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kaohsiung","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taiwan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 April 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 April 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aciids2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aciids.pwr.edu.pl\/2026\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}