The main components in the study of multi-agent systems in the field of IoT are intelligent agents and contexts. From the viewpoint of each agent, the primary objective is to choose actions that maximize the agent's future utility. The choice of a correct action depends on a process of complex data collection from context. Using a set of neuroscience model we consider the receptory field to slice the context area and to group the appropriate sensors. To permit the phenomenological analysis and consequent cognitive evaluation, we have applied Bellmund and Doeller’s model of hippocampus. This session describes only the phenomenological layer architecture. From a scientific point of view there is a novel fusion between fluent calculus and neural network ensemble. Such net are used to associate to situations’ fluent the appropriate schema of actions.
Keywords—IoT, Neural Network Ensemble, fluent Calculus, Place Map, Grid map, receptive field.
He is a research and development executive with wide and successful experience in the management of realization of cloud computing and AI. Strong international credentials and sensitivity to lead a substantial team from diverse backgrounds and cultures together with the understanding of what it takes to conduct business successfully across different audiences and markets.
Strong team building and engaging skills to excel in large, fast-moving organizations, ability to build effective work environments and motivate team members to grow in their roles and achieve personal and company success in a low-ego atmosphere.
He is working on an intelligent architecture to integrate connectionism with logical techniques having in the core a creative digital mind that thinks to the general improvement of the techno-structure as a whole.