From numbers to symbols to knowledge structures: artificial intelligence perspectives on the classification task

The general information-processing task of classification is considered and reviewed from the perspectives of the knowledge-based-reasoning, pattern-recognition, and connectionist paradigms in artificial intelligence, paying special attention to knowledge-based classificatory problem solving. The authors trace the evolution of the mechanisms for classification as the computational complexity of the problem increases, from numerical parameter-setting schemes, through those using intermediate abstractions and then relations between symbols, and finally to complex symbolic structures that explicitly incorporate domain knowledge.>