architecture.md

Architecture

Introduction

The WholeBrainedIntelligence (WBI) system architecture is designed to emulate various cognitive processes. It consists of multiple modules that work together to provide comprehensive AI capabilities.

System Overview

The WBI system is composed of the following main components:

• Self-Awareness Module
• Heuristic Pattern Recognition Module
• Counterfactual Simulation Module
• Value Affection Module
• Empathic Interaction Module
• Learning from Experience Module

Component Interaction

Each module interacts with others through defined interfaces and data exchanges. The following diagram illustrates the overall architecture and interactions between modules:

Detailed Components

Self-Awareness Module

• Purpose: Provides self-monitoring and reflective capabilities.
• Design: Uses internal state tracking and meta-cognition techniques.

Heuristic Pattern Recognition Module

• Purpose: Detects patterns and anomalies in data.
• Design: Implements heuristic algorithms and machine learning models.

Counterfactual Simulation Module

• Purpose: Simulates alternate scenarios to aid decision-making.
• Design: Utilizes simulation techniques and probabilistic models.

Value Affection Module

• Purpose: Evaluates and assigns value to different outcomes.
• Design: Combines reinforcement learning and value-based decision-making.

Empathic Interaction Module

• Purpose: Facilitates empathetic responses and interactions.
• Design: Integrates natural language processing and emotion recognition.

Learning from Experience Module

• Purpose: Enhances learning from past experiences.
• Design: Applies reinforcement learning and experience replay techniques.

Future Enhancements

• Integration with quantum computing resources.
• Advanced predictive analytics capabilities.

For more details on each module, refer to their respective documentation.