{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T13:41:20Z","timestamp":1777902080497,"version":"3.51.4"},"reference-count":18,"publisher":"SAGE Publications","issue":"10","license":[{"start":{"date-parts":[[2003,10,1]],"date-time":"2003-10-01T00:00:00Z","timestamp":1064966400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["SIMULATION"],"published-print":{"date-parts":[[2003,10]]},"abstract":"<jats:p>Multiply-sectioned Bayesian networks (MSBNs) extend Bayesian networks to graphical models for multiagent probabilistic reasoning. The empirical study of algorithms for manipulations of MSBNs (e.g., verification, compilation, and inference) requires experimental MSBNs. As engineering MSBNs in large problem domains requires significant knowledge and engineering effort, the authors explore automatic simulation of MSBNs. Due to the large domain over which an MSBN is defined and a set of constraints to be satisfied, a generate-and-test approach toward simulation has a high rate of failure. The authors present an alternative approach that treats the simulation process as a sequence of decisions. They constrain the space of each decision so that backtracking is minimized and the outcome is always a legal MSBN. A suite of algorithms that implements this approach is presented, and experimental results are shown.<\/jats:p>","DOI":"10.1177\/0037549703039950","type":"journal-article","created":{"date-parts":[[2004,4,21]],"date-time":"2004-04-21T20:41:37Z","timestamp":1082580097000},"page":"545-567","source":"Crossref","is-referenced-by-count":3,"title":["Simulation of Graphical Models for Multiagent Probabilistic Inference"],"prefix":"10.1177","volume":"79","author":[{"given":"Y.","family":"Xiang","sequence":"first","affiliation":[{"name":"Department of Computing and Information Science College of Physical and                        Engineering Science University of Guelph Guelph, Ontario Canada N1G 2W1"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"X.","family":"An","sequence":"additional","affiliation":[{"name":"School of Computer Science University of Waterloo Waterloo, Ontario                        Canada N2L 3G1"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"N.","family":"Cercone","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science Dalhousie University 6050 University Avenue                        Halifax, Nova Scotia Canada B3H 1W5"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2003,10,1]]},"reference":[{"key":"atypb1","unstructured":"[1] Sycara, K. 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Wuillemin. 2000. Top-down construction and repetitive structures representation in Bayesian networks . In Proceedings of the 13th International Florida Artificial Intelligence Research Society Conference, Orlando, USA."},{"key":"atypb18","doi-asserted-by":"crossref","unstructured":"[18] Xiang, Y. 2002. Probabilistic reasoning in multi-agent systems: A graphical models approach. 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