Knowledge-based decision intelligence in street lighting management

As the availability of computational power and communication technologies increases, Humans systems are able to tackle increasingly challenging decision problems. Taking decisions over incomplete visions a situation is particularly calls for set intertwined skills that must be put into place under clear rationale. This work addresses how deliver autonomous management public street lighting network, optimize energy consumption without compromising light quality patterns. Our approach grounded in an holistic methodology, combining semantic Artificial Intelligence principles define methods artefacts supporting taken context domain. That is, domain with absence data explicit assertions.