Integrated connectionist models: building AI systems on subsymbolic foundations

Symbolic artificial intelligence is motivated by the hypothesis that symbol manipulation is both necessary and sufficient for intelligence. In symbolic systems, knowledge is encoded in terms of explicit symbolic structures, and inferences are based on handcrafted rules that sequentially manipulate these structures. Such systems have been quite successful, for example, in modeling in-depth natural language processing, episodic memory, and symbolic problem solving. However, much of the inferencing for everyday natural language understanding appears to take place immediately, without conscious control, apparently based on associations with past experience. This type of reasoning is difficult to model in the symbolic framework. In contrast, subsymbolic (distributed connectionist) networks represent knowledge in terms of correlations, coded in the weights of the network. For a given input, the network computes the most likely answer given its past experience. A number of human-like information processing properties such as learning from examples, context sensitivity, generalization, robustness of behavior, and intuitive reasoning emerge automatically in subsymbolic systems. The major motivation for subsymbolic AI, therefore, is to give a better account for cognitive phenomena that are statistical, or intuitive, in nature