Knowledge Engineering

The main similarity between Artificial Neural Networks (ANN) and Case Based Reasoning (CBR) is that both do not need an explicit model. Both CBR and ANN techniques do not have to go through the knowledge-acquisition bottleneck. ANN is essentially a data mining technique, which can work directly from the data.

But, the main criticism against ANN is that it works as a “Black Box”, so they suffer from a lack of transparency. Validity of the systems decision cannot be judged because of the nature of the inner workings, the output of the network is a function of weighted vectors that depends on the network's architecture and the learning mode used. So, it becomes very difficult to use ANN for diagnosis applications, as most of the diagnosis needs an explanation for the result obtained.

ANN are not suitable when background domain knowledge has to be taken into account, whereas in CBR domain knowledge can be incorporated in the form of knowledge-guided clustering. Neural networks cannot cope with complex structures and in order to perform well the coverage of the domain has to be exhaustive during the "learning" phase. CBR does not need an exhaustive coverage of the domain, as cases can be added to the case-base incrementally.