The aim of the AI techniques embedded in an intelligent decision support system is to enable these tasks to be performed by a computer, while emulating human capabilities as closely as possible.
[10] They typically combine knowledge of a particular application domain with an inference capability to enable the system to propose decisions or diagnoses.
Accuracy and consistency can be comparable to (or even exceed) that of human experts when the decision parameters are well known (e.g. if a common disease is being diagnosed), but performance can be poor when novel or uncertain circumstances arise.
For example, intelligent agents[11][12] that perform complex cognitive tasks without any need for human intervention have been used in a range of decision support applications.
A range of AI techniques such as case based reasoning, rough sets[14] and fuzzy logic have also been used to enable decision support systems to perform better in uncertain conditions.