Blackboard Architecture for Intelligent Embedded Systems
Abstract from thesisIt can be argued that the future of machines lies in embedding intelligent systems within them. Unfortunately, sophisticated artificial intelligence (AI) software is usually large and complicated, requiring powerful processors that are not practical in most embedded systems owing to cost, size, and heat production. One solution is to distribute the intelligent processing across many less powerful processors. This research has investigated the suitability, characteristics, and potential of a distributed blackboard system as an architecture for the implementation of complex AI software in an embedded distributed processing network.
A distributed blackboard system called DARBS (Distributed Algorithmic and Rule-based Blackboard System) has been implemented on a distributed processing network comprising up to 18 personal computers. Using the TileWorld environment as a test-bed, the distributed set-up was found to outperform a non-distributed one, although both were non-optimal. The speedup factor of the distributed blackboard system increases up to a maximum as the number of agent processors (APs) increases, after which it drops a little and levels off. To obtain maximum speedup for a given number of agents, it was found that an even distribution of agents across the APs is required, while avoiding saturation of the blackboard. The optimum number of agents per AP was found to be two, with blackboard saturation starting at eight APs. Based on these findings, an embedded version of DARBS (called emDARBS) was designed and implemented on a SARNet parallel processing network of low-cost StrongArm processors. This implementation has demonstrated that a distributed blackboard system is suitable for embedded distributed processing networks, but that some changes are required to tailor it for embedded systems. The potential for embedding intelligent systems in everyday machines has thus been demonstrated.
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