{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T07:14:51Z","timestamp":1781248491823,"version":"3.54.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:p>One problem to solve in the context of information fusion, decision-making, and other artificial intelligence challenges is to compute justified beliefs based on evidence. In real-life examples, this evidence may be inconsistent, incomplete, or uncertain, making the problem of evidence fusion highly non-trivial. In this paper, we propose a new model for measuring degrees of beliefs based on possibly inconsistent, incomplete, and uncertain evidence, by combining tools from Dempster-Shafer Theory and Topological Models of Evidence. Our belief model is more general than the aforementioned approaches in two important ways: (1) it can reproduce them when appropriate constraints are imposed, and, more notably, (2) it is flexible enough to compute beliefs according to various standards that represent agents' evidential demands. The latter novelty allows the users of our model to employ it to compute an agent's (possibly) distinct degrees of belief, based on the same evidence, in situations when, e.g, the agent prioritizes avoiding false negatives and when it prioritizes avoiding false positives. Finally, we show that computing degree of belief with this model is #P-complete in general.<\/jats:p>","DOI":"10.24963\/kr.2023\/54","type":"proceedings-article","created":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T22:27:47Z","timestamp":1690842467000},"page":"552-561","source":"Crossref","is-referenced-by-count":2,"title":["A Belief Model for Conflicting and Uncertain Evidence: Connecting Dempster-Shafer Theory and the Topology of Evidence"],"prefix":"10.24963","author":[{"given":"Daira","family":"Pinto Prieto","sequence":"first","affiliation":[{"name":"University of Amsterdam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronald","family":"de Haan","sequence":"additional","affiliation":[{"name":"University of Amsterdam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayb\u00fcke","family":"\u00d6zg\u00fcn","sequence":"additional","affiliation":[{"name":"University of Amsterdam"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"20th International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}","theme":"Artificial Intelligence","location":"Rhodes, Greece","acronym":"KR-2023","number":"20","sponsor":["Artificial Intelligence Journal","Principles of Knowledge Representation and Reasoning Inc.","Academic College of Tel-Aviv","European Association for Artificial Intelligence","National Science Foundation"],"start":{"date-parts":[[2023,9,2]]},"end":{"date-parts":[[2023,9,8]]}},"container-title":["Proceedings of the Twentieth International Conference on Principles of Knowledge Representation and Reasoning"],"original-title":[],"deposited":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T22:28:38Z","timestamp":1690842518000},"score":1,"resource":{"primary":{"URL":"https:\/\/proceedings.kr.org\/2023\/54"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2023,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/kr.2023\/54","relation":{},"subject":[],"published":{"date-parts":[[2023,9]]}}}