{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:35:28Z","timestamp":1761176128003,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Pooling multiple beliefs and opinions is often crucial in multi-agent systems. Specifically, we consider pooling in the context of multi-hypothesis social learning problems, where agents gather evidence and pool information to determine the true hypothesis. Whilst past research has largely focused on designing probability pooling operators to satisfy certain desirable properties, there has been limited exploration of learning operators directly from data. The conventional method, relying on well-motivated axioms and heuristics for pooling, constrains the solution space for operators and choosing between different operators often requires domain specific knowledge. We introduce a machine learning approach to opinion pooling that derives operators via end-to-end reinforcement learning incorporating DeepSets neural networks. We provide empirical results showing that our approach outperforms existing baselines across a range of social learning contexts and exhibits robust generalisation to the number of agents pooled in a single operation. Furthermore, we provide an analysis of the learned neural network representations, offering insights into optimal opinion pooling strategies.<\/jats:p>","DOI":"10.3233\/faia250839","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:43:45Z","timestamp":1761126225000},"source":"Crossref","is-referenced-by-count":0,"title":["DeepSets Reinforcement Learning for Opinion Pooling"],"prefix":"10.3233","author":[{"given":"Jack","family":"Butler","sequence":"first","affiliation":[{"name":"University of Bristol"}]},{"given":"Chanelle","family":"Lee","sequence":"additional","affiliation":[{"name":"University of Bristol"}]},{"given":"Jonathan","family":"Lawry","sequence":"additional","affiliation":[{"name":"University of Bristol"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250839","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:43:45Z","timestamp":1761126225000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250839"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250839","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}