A Hybrid Artificial Intelligence, Machine Learning, and Control Algorithm Integration Framework for Embedded Systems using Semantic Web Technology

Abstract

A framework to integrate structurally different artificial intelligence, machine learning, and control algorithms is combined with an execution framework to create a powerful embedded system development platform. Control, decision, or algorithms providing an emulation of intelligent behavior in both declarative (interpreted) and imperative (compiled) paradigms can now be combined, for example Prolog and neural networks, respectively. This hybridization of algorithms provides more efficient overall control of systems in terms of resources such as compute cycles, network bandwidth and throughput, and memory speed and capacity. By providing an execution framework and control software that is native to embedded system and cloud architectures, and supports interactivity and time synchronization, the true utility of cloud computing and "big data systems" can be increased.