Massive Parallelism in Logic

Franz Kurfed

The capability of drawing new conclusions from available information still is a major challenge for computer systems. Whereas in more formal areas like automated theorem proving the ever-increasing computing power of conventional systems also leads to an extension of the range of problems which can be treated, many real-world reasoning problems involve uncertain, incomplete, and inconsistent information, possibly from various sources like rule and data bases, interaction with the user, or raw data from the real world. For this kind of applications inference mechanisms based on or combined with massive parallelism, in particular connectionist techniques show some promise. This contribution concentrates on three aspects of massive parallelism and inference: first, the potential of parallelism in logic is investigated, then an massively parallel inference system based on connectionist techniques is presented 1 and finally the combination of neural-network based components with symbolic ones as pursued in the project WlNA is described.

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