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Consciousness is a tremendously complex phenomenon. We examined the configurations and functions of an autonomously adaptive system that can adapt to an environment without a teacher to understand this complex phenomenon in the easiest way possible, and proposed a modeling method of consciousness on the system. In modeling of consciousness, it is important to note the difference between phenomenal consciousness and functional consciousness. To clarify the difference, a model with two layers, a physical layer and a logical layer, is proposed. The functions of primitive consciousness on the autonomously adaptive system were clarified on the model. The physical layer is composed of an artificial neural node. All signals are processed in detail by the neural nodes. Contrarily, minimum information, necessary for the system to adapt itself, selected from the physical layer composes the logical layer. The operations in the logical layer are represented by interactions between only the selected information. Our daily conscious phenomenon is expressed on the logical layer.
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Although many models of consciousness have been proposed from various viewpoints, they have not been based on learning activities in a whole system with the capabilities of autonomous adaptation. We have been investigating a simplified system using artificial neural nodes to clarify the functions and configuration needed for learning in a system that autonomously adapts to the environment. We demonstrated that phenomenal consciousness is explained using a method of "virtualization" in the information system and that learning activities in a whole system adaptation are related to consciousness. However, we have not sufficiently clarified the learning activities of such a system. Consciousness is basically modeled as a system-level learning activity to modify both its own configuration and states in autonomous adaptation through investigating learning activities as a whole system. The model not only explains the time delay in Libet's experiment, but is also positioned as an improved model of Global Workspace Theory (GWT).
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In developing a humanoid robot, there are two major objectives. One is developing a physical robot having body, hands, and feet resembling those of human beings and being able to similarly control them. The other is to develop a control system that works similarly to our brain, to feel, think, act, and learn like ours. In this article, an architecture of a control system with a brain-oriented logical structure for the second objective is proposed. The proposed system autonomously adapts to the environment and implements a clearly defined “consciousness” function, through which both habitual behavior and goal-directed behavior are realized. Consciousness is regarded as a function for effective adaptation at the system-level, based on matching and organizing the individual results of the underlying parallel-processing units. This consciousness is assumed to correspond to how our mind is “aware” when making our moment to moment decisions in our daily life. The binding problem and the basic causes of delay in Libet’s experiment are also explained by capturing awareness in this manner. The goal is set as an image in the system, and efficient actions toward achieving this goal are selected in the goal-directed behavior process. The system is designed as an artificial neural network and aims at achieving consistent and efficient system behavior, through the interaction of highly independent neural nodes. The proposed architecture is based on a two-level design. The first level, which we call the “basic-system,” is an artificial neural network system that realizes consciousness, habitual behavior and explains the binding problem. The second level, which we call the “extended-system,” is an artificial neural network system that realizes goal-directed behavior.
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A basic structure and behavior of a human-like AI system with conscious like functions is proposed. The system is constructed completely with artificial neural networks (ANN), and an optimal-design approach is applied. The proposed system using recurrent neural networks (RNN) which execute learning under dynamic equilibrium is a redesign of ANN in the previous system. The redesign using the RNNs allows the proposed brain-like autonomous adaptive system to be more plausible as a macroscopic model of the brain. By hypothesizing that the “conscious sensation” that constitutes the basis for phenomenal consciousness, is the same as “state of system level learning”, we can clearly explain consciousness from an information system perspective. This hypothesis can also comprehensively explain recurrent processing theory (RPT) and the global neuronal workspace theory (GNWT) of consciousness. The proposed structure and behavior are simple but scalable by design, and can be expanded to reproduce more complex features of the brain, leading to the realization of an AI system with functions equivalent to human-like consciousness.