limitations in pattern recognition and problem solving
This paper discusses the standard techniques used by problem solving and pattern recognition programs. It is pointed out that evaluation functions (often called discriminant functions) lie at the heart of these programs. Simplicity appears to be an important property of evaluation functions because evaluation functions which are both relatively accurate and efficient are in some sense simple. In addition, simple evaluation functions are easier to learn because there are fewer parameters to estimate. The real difficulty is that no general method exists which extracts a good set of features for a particular problem. Even if this were possible one is still faced with the task of combining these features to obtain the answer. In the case of pattern recognition this combination takes the form of connectives such as arithmetic operations or Boolean operations. However, in the case of problem solving, things are much more complicated since features are at the bottom of a search procedure which looks at many different problem states. This presents the difficulty of making good use of such a large volume of information.
pattern recognition problem solving evaluation functions artificial intelligence search space heuristics representation feature extraction