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Future building automation will require complex (humanlike) perception and decision-making processes not being feasible with classical approaches. In this paper, we address both the perception and the decision-making process and present an alerting model that reacts to perceived situations in a building with decisions about possible alerts. Perception is based on the neurosymbolic information-processing model, which detects candidate alerts. Integrated with perception, decision making is based on the rule model of the Rule Markup Language, which computes alerts to relevant building occupants about current opportunities and risks. A general model of neurosymbolic alerting rules is developed and exemplified with a use case of building alerts.