NEURO-BAYESIAN ARCHITECTURE FOR IMPLEMENTING ARTIFICIAL GENERAL INTELLIGENCE

摘要

The present disclosure envisages a processor architecture designed tor artificial general intelligence operations. The engine for Neuro-Bayesian teaming (eN-BLe) further includes a hierarchical Neuro Bayesian Network module, a reinforcement learning module, a supervised learning, module, and a planning, imagination, and simulation module, for planning, imagination, and decision making under uncertainty. The engine for Neuro-Bayesian learning is communicably coupled to a user application and receives input data from the user application. The hierarchical Neuro-Bayesian Network (H-NBN) acts as a probabilistic internal model of an application or unknown environment. The H-NBN is capable of probabilistic and Bayesian inference, prediction, and unsupervised learning. Thereafter, the outputs of the H-NBN are provided to supervised NBNs for classification or regression of input states. Additionally, the output of the H-NBN is provided to the reinforcement learning module, which in turn comprises Value-NBNs (V-NBNs) and Policy-NBNs (P-NBNs), to compute expected reward and select optimal actions under uncertainty.