Adaptive Neural Network for Human Affect Modeling
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Solution Overview
Problem
Current cognitive architectures lack comprehensive integration of perception, problem-solving, and natural language, failing to model flexible human behaviors and emotions effectively, and are not amenable to implementation as stand-alone or hybrid neuro-AI systems.
Innovation Solution
The development of an adaptive neural network (ANN) system, 'human affect computer modeling' (HACM) that recognizes, interprets, and simulates human reactions and affects through a sensory input collection, cognitive learning, and execution module, using a Cerebellar Model Articulation Controller (CMAC) for real-time reinforcement learning, enabling the prediction and reproduction of human affects and reactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional cognitive architectures (ACT-R, Soar) are used, then rule-based problem solving is achieved, but flexibility in responding to dynamic internal or external changes is lost
Solution Approach 1:
The patent applies dynamics by transitioning from static rule-based systems to a dynamic neural network architecture that continuously adapts its weights and structure. The HACM system uses learning control with multiple parallel processing pathways that can dynamically adjust to new inputs and contexts, enabling flexible responses to changing conditions without requiring complex rule reconfiguration.
Solution Approach 2:
The system implements self-service through autonomous learning and adaptation mechanisms. The neural network automatically adjusts its parameters and pathways based on input data without external intervention, performing self-organization and continuous optimization of its cognitive models, thereby reducing the need for manual system reconfiguration.
2Adaptability or versatility
If comprehensive brain-like architecture is implemented, then integration of physiology, anatomy and functionalities is achieved, but implementation complexity increases
Solution Approach 1:
The patent segments the comprehensive brain-like architecture into distinct functional modules including sensory input collection, cognitive learning, execution, and optimization components. Each module handles specific cognitive functions independently, allowing the system to achieve comprehensive modeling capability while managing implementation complexity through modular design and parallel processing.
3Adaptability or versatility
If rigid propositional representations are used, then structural constraints are maintained, but modeling of flexible human behavior is limited
Solution Approach 1:
The system employs parameter changes by transforming rigid propositional representations into flexible vector-based representations with adjustable dimensions and properties. The neural network modifies these parameters dynamically during learning, enabling the system to model flexible human behaviors while maintaining sufficient structural constraints through the underlying vector space architecture.
Data Source
AI summary
Continuously learning and optimizing artificial intelligence (AI) adaptive neural network (ANN) computer modeling methods and systems, designated human affect computer modeling (HACM) or affective neuron (AN), and, more particularly, to AI methods, systems and devices that can recognize, interpret, process and simulate human reactions and affects such as emotional responses to internal and external sensory stimuli, that provides real-time reinforcement learning modeling that reproduces human affects and/or reactions, wherein the human affect modeling (HACM) can be used singularly or collectively to modeling and predict complex human reactions and affects.