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--- title: "decision network" source: https://www.jemoka.com/posts/kbhdecision_networks/ --- A decision network is a Baysian Network which is used to make decisions based on optimizing utility. To solve a problem, we iterate through all possible decision parameters to find the one that maximizes utility. Nodes chance nodes: random variables — some inputs we can observe, some are latent variables we can’t observe — circles action nodes: what we have control over — squares utility nodes: output, what the results would be; we typically sum utilities together if you have multiple of them — diamonds Edges conditional edge - arrows to chance nodes: conditional probability edges informational edge - arrows to action nodes: this information is used to inform choice of action functional edge - arrows to utility nodes: computes how the action affects the world Example For \(U\), for instance, you can have a factor that loks ilke: