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Decision analysis networks

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dc.contributor.authorBermejo, Iñigo
dc.contributor.authorDíez Vegas, Francisco Javier
dc.contributor.authorLuque Gallego, Manuel
dc.date.accessioned2024-05-20T11:43:16Z
dc.date.available2024-05-20T11:43:16Z
dc.date.issued2018-05
dc.description.abstractThis paper presents decision analysis networks (DANs) as a new type of probabilistic graphical model. Like influence diagrams (IDs), DANs are much more compact and easier to build than decision trees and can represent conditional independencies. In fact, for every ID there is an equivalent symmetric DAN, but DANs can also represent asymmetric problems involving partial orderings of the decisions (order asymmetry), restrictions between the values of the variables (domain asymmetry), and conditional observability (information asymmetry). Symmetric DANs can be evaluated with the same algorithms as IDs. Every asymmetric DAN can be evaluated by converting it into an equivalent decision tree or, much more efficiently, by decomposing it into a tree of symmetric DANs. Given that DANs can solve symmetric problems as easily and as efficiently as IDs, and are more appropriate for asymmetric problems—which include virtually all real-world problems—DANs might replace IDs as the standard type of probabilistic graphical model for decision support and decision analysis. We also argue that DANs compare favorably with other formalisms proposed for asymmetric decision problems. In practice, DANs can be built and evaluated with OpenMarkov, a Java open-source package for probabilistic graphical models.en
dc.description.versionversión final
dc.identifier.doi10.1016/j.ijar.2018.02.007
dc.identifier.issn0888-613X
dc.identifier.urihttps://hdl.handle.net/20.500.14468/12465
dc.journal.titleInternational Journal of Approximate Reasoning
dc.journal.volume96
dc.language.isoen
dc.publisher['Decision analysis', 'Decision trees', 'Influence diagrams', 'Probabilistic graphical models', 'Asymmetric decision problems']
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInteligencia Artificial
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.titleDecision analysis networkses
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublicationc6032e20-a1d0-49b9-92e3-5c9f624ab143
relation.isAuthorOfPublicatione94799f0-bdd0-45be-b6b2-426269f6ee46
relation.isAuthorOfPublication.latestForDiscoveryc6032e20-a1d0-49b9-92e3-5c9f624ab143
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