Knowledge Engineering

Optimization is the process of finding the best solution (optimal solution) for a given problem. It is generally misunderstood that optimization is synonymous to maximization. The truth is that optimization may represent either maximization or minimization depending upon the type of problem at hand.

For example, the problem of finding a solution that provides the maximum profit for a given set of resources and constraints is a maximization problem. The same problem could be modeled as a minimization problem to find a solution that provides the minimum cost for a given set of resources and constraints.

Optimization can be of two types:

1. Global optimization and


2. Local optimization

The global optimum is always unique whereas the local optimum could change for every run of the optimization process depending on the initial solution set. While global optimum is desirable, in some cases, it may not be possible to find the global optimum or to verify whether a given solution is the global optimum. Global optimization also requires more computational power.

Even though various optimization techniques exist, the best technique depends on the type of problem, the domain and the business requirements. The following are some of the well known optimization techniques:

• Linear Programming


• Integer Programming


• Mixed-Integer Programming


• Constraint Programming


• Genetic Algorithms


• Simulated Annealing


• Hopfield Neural Network

Image thinning is the process of reducing the width of a digitised pattern to just a single pixel so that the topological properties are preserved. The output of thinning is called Skeleton. A skeleton provides an abstraction of the global shape of the object. A skeleton normally requires less storage space compared to the original pattern while it preserves the essential structural information of the pattern.

Neural networks has been successfully applied to image thinning problems and pattern recognition applications like Optical Character Recognition (OCR) and medical imaging applications.

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.