The connection machine
PARKA: parallel knowledge representation on the Connection Machine
PARKA: parallel knowledge representation on the Connection Machine
Integrating Marker Passing and Problem Solving: A Spreading Activation Approach to Improved Choice in Planning
The SNAP-1 parallel AI prototype
ISCA '91 Proceedings of the 18th annual international symposium on Computer architecture
Random walks on text structures
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
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The semantic network array processor (SNAP) is a specialized, highly parallel architecture for knowledge representation and reasoning. The instruction set has been carefully designed to reflect the requirements of semantic network processing. SNAP is a marker propagation architecture, where the passing of markers between cells plays a fundamental role. The movement of markers between cells is controlled by a set of propagation rules. Various reasoning mechanisms were implemented using these propagation rules. A simulator was developed, and knowledge processing examples, such as inheritance, recognition, and classification, were tested. By comparing the simulation results with the same examples run on the Connection Machine, it was found that SNAP outperforms the Connection Machine over a broad range of knowledge processing examples by a factor of 1000 or more.