SBIR-STTR Award

The objective of this SBIR project is to develop an attached processor called an "agent based computing" module (ABC Machine) that is optimized to convert information into knowledge and enable "cognitive computing" for higher understanding. The architecture of the ABC Machine is based upon a concept called "statistical dataflow computing" and it operates in local contexts over string operators. It is motivated in part by analyzing the biochemical processing in biological cells, but with a goal to provide an architecture for computing problems not easily solved by traditional machines

This SBIR will develop a new class of computer architecture called an ìagent based computingî module (ABC Machine) that enables ìcognitive computingî algorithms to be implemented effectively on a large scale. The ABC Machine is a biologically inspired architecture derived from the field of ìmembrane computingî and is also based upon ìstatistical dataflow computingî. It operates in local contexts over string operators. The ABC Machine is motivated by analyzing the biochemical processing in cells. The architecture is suited for computing problems not easily solved by traditional machines. It has the properties of very high parallelism, distributed and redundant processing, and graceful degradation. Phase 1 found the ABC Machine to be both feasible and attractive for connectionist problems, symbolic computing problems with fuzzy and deep search spaces, and for machine learning to these problems. For Phase 2, the ABC Machine will be emulated on a High Performance Cluster (HPC) machine to achieve near term results on complex cognitive problems. Phase 1 demonstrated advantages over traditional AI algorithms on cenventional machines in the following ways

Keywords:
Artificial Intelligence, Cognitive Computing, High Performance Cluster, Biologically Inspired Computer Architecture, Pattern Recognition, Web Service