A parallel architecture for signal understanding through inference on uncertain data
This paper describes an architecture for rule-based interpretation of uncertain data, which is currently under development at our labs. Inference on uncertain input facts is a central topic in Al, with application, e.g., to the syntactic-semantic layers of speech understanding systems. The severe requirements of real-time applications dictate a parallel approach to this problem. The description covers the main aspects related to parallelism and communication at the three levels which have interacted in the design of this architecture: the hardware machine, a highly-parallel homogeneous structure of processing element — memory pairs interconnected by a fast packet-switching network; the programming language, which is a dialect of Lisp augmented with asynchronous message passing primitives; the inferential algorithm, which unifies goal-driven and data-driven strategies under a score-guided search control. Rules are mapped into a set of processes which cooperate by exchanging, via the primitives and the network mentioned above, messages corresponding to succinct representations of intermediate deductions.
KeywordsUncertain Data Physical Machine Local Scheduler Deductive Process Processing Element Array
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