Adaptive Neural Network Architecture for Human Affect Modeling
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Solution Overview
Problem
Current cognitive architectures lack comprehensive integration of perception, problem-solving, and emotional responses, failing to model flexible human behaviors and adapt to dynamic environments.
Innovation Solution
The development of an adaptive neural network (ANN) system, designated 'human affect computer modeling' (HACM), which integrates sensory input, cognitive learning, and execution modules to recognize, interpret, and simulate human reactions and affects, utilizing real-time reinforcement learning and CMAC neural networks for parallel processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cognitive architectures (ACT-R, Soar) are used, then rule-based control and production systems are established, but the system lacks flexibility to respond to dynamic internal or external changes
Solution Approach 1:
The patent implements a hybrid architecture that combines static rule-based production systems with dynamic neural network components. The neural networks continuously learn and adapt to dynamic internal and external changes, while the production system provides stable rule-based control, resolving the contradiction between reliability and adaptability.
Solution Approach 2:
The patent merges two different architectural approaches: traditional production systems (ACT-R, Soar) and neural network models. This combination allows the system to leverage the strengths of both - the interpretability and control of production systems and the adaptability and learning capacity of neural networks.
2Stability of the object's composition
If rigid propositional representations are used, then structural constraints are maintained, but the system cannot model flexible human behaviors
Solution Approach 1:
The patent transitions from rigid propositional representations to continuous-valued neural network representations. This parameter change allows the system to maintain structural constraints through architecture design while gaining flexibility through continuous activation values and distributed representations.
Solution Approach 2:
The patent creates a composite representational system that combines symbolic propositional representations with subsymbolic neural network activations. This composite approach maintains the interpretability of symbolic representations while incorporating the flexibility of continuous-valued neural activations.
3Device complexity
If perception is treated as a peripheral activity separate from problem solving, then modular architecture is maintained, but comprehensive integration of perception and problem solving is not achieved
Solution Approach 1:
The patent merges previously separate perception and problem-solving modules into an integrated architecture. Neural networks serve as both perception processors and problem-solving engines, with shared representations and continuous interaction between perceptual input and cognitive processing.
Solution Approach 2:
The patent implements multi-functional neural network components that simultaneously perform perception, representation, and problem-solving functions. The same neural network structures are used across different cognitive tasks, achieving comprehensive integration while maintaining architectural efficiency.
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.