AGI Brain: A Learning and Decision Making Framework for Artificial General Intelligence Systems Based on Modern Control Theory
In this paper a unified learning and decision making framework for artificial general intelligence (AGI) based on modern control theory is presented. The framework, called AGI Brain, considers intelligence as a form of optimality and tries to duplicate intelligence using a unified strategy. AGI Brain benefits from powerful modelling capability of state-space representation, as well as ultimate learning ability of the neural networks. The model emulates three learning stages of human being for learning its surrounding world. The model was tested on three different continuous and hybrid (continuous and discrete) Action/State/Output/Reward (ASOR) space scenarios in deterministic single-agent/multi-agent worlds. Successful simulation results demonstrate the multi-purpose applicability of AGI Brain in deterministic worlds.
KeywordsArtificial general intelligence Modern control theory Optimization Implicit and explicit memory Shared memory Stages of learning Planning Policy Multi-Agent Emotions Decision making Continuous and hybrid ASOR space
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