G.O.D Framework

Introduction

The ai_emotional_core.py module acts as the foundational layer for emotional intelligence within the G.O.D Framework. It provides a central AI component for processing, modeling, and understanding emotional dynamics, enabling the framework to engage in empathetic reasoning and adaptive responses.

Designed for human-AI interaction systems, this module powers empathetic conversational AI, sentiment-driven recommendations, and emotional analytics across industries like customer service, healthcare, and entertainment.

Purpose

• Emotional Knowledge Base: Maintain a representation of emotional states and their relationships.
• Emotion Fusion: Integrate multimodal emotive signals (text, audio, visual) into a cohesive emotional response.
• Emotional State Modeling: Track and update user emotional states dynamically.
• Empathetic Reasoning: Enable AI systems to exhibit empathetic behavior based on emotions.
• Interpersonal AI Interaction: Improve the quality of AI interactions with emotional awareness.

Key Features

• Dynamic Emotion Modeling: Continuously updates the emotional state of users and adapts AI behavior accordingly.
• Emotion Fusion Engine: Combines data from textual, audio, and visual sources to produce a unified emotional state.
• Empathy Synthesizer: Enables AI systems to generate emotionally appropriate responses in real-time.
• Scalable Architecture: Modular and extensible to incorporate new emotional models or datasets.
• Compatibility: Easily integrates with other AI modules like sentiment analysis, feedback loops, and explainability engines.

Logic and Implementation

The core logic combines emotion detection and merging mechanisms with a dynamic emotional state updater. It maintains a central emotional repository that continuously syncs with the user's interactions. The emotional fusion engine calculates a weighted score for each modality (text, audio, visual) to determine the most accurate emotional state.

An example implementation is as follows:

            class EmotionalCore:
                """
                Central Emotional Core for managing, understanding, and modeling emotions in AI systems.
                """

                def __init__(self):
                    self.emotional_state = {
                        "anger": 0.0,
                        "joy": 0.0,
                        "sadness": 0.0,
                        "fear": 0.0,
                        "neutral": 1.0
                    }
                    self.weighted_modalities = {"text": 0.5, "audio": 0.3, "visual": 0.2}
                    self.current_emotion = "neutral"

                def update_emotion(self, modality, emotion_scores):
                    """
                    Update the emotional state based on modality-specific emotion scores.
                    :param modality: Source of emotion (text, audio, visual).
                    :param emotion_scores: Dictionary of emotion probabilities (e.g., {"joy": 0.7, "sadness": 0.3}).
                    """
                    if modality in self.weighted_modalities:
                        weight = self.weighted_modalities[modality]
                        for emotion, score in emotion_scores.items():
                            self.emotional_state[emotion] += score * weight
                        self.normalize_emotional_state()

                def normalize_emotional_state(self):
                    """
                    Normalize emotion probabilities to ensure they sum to 1.
                    """
                    total = sum(self.emotional_state.values())
                    self.emotional_state = {k: v / total for k, v in self.emotional_state.items()}
                    self.current_emotion = max(self.emotional_state, key=self.emotional_state.get)

                def get_current_emotion(self):
                    """
                    Return the current dominant emotional state.
                    :return: Dominant emotion (string).
                    """
                    return self.current_emotion

            if __name__ == "__main__":
                core = EmotionalCore()
                # Example text-based emotion update
                text_scores = {"joy": 0.8, "sadness": 0.2}
                core.update_emotion("text", text_scores)
                print(f"Updated Emotion State: {core.emotional_state}")
                print(f"Dominant Emotion: {core.get_current_emotion()}")
            

Dependencies

This module relies on the following libraries and frameworks:

• numpy: Efficient numerical computations for emotional state normalization.
• transformers (optional): Integration with sentiment and emotion models for multimodal analysis.
• tensorflow/torch (optional): For custom model integration.

Usage

The ai_emotional_core.py script can be used to manage emotional intelligence in AI-driven systems. Key steps include:

1. Initialize the EmotionalCore object.
2. Feed emotion probabilities for each modality using update_emotion().
3. Retrieve the current dominant emotion with get_current_emotion().

            core = EmotionalCore()
            core.update_emotion("text", {"joy": 0.75, "neutral": 0.25})
            print(f"Dominant Emotion: {core.get_current_emotion()}")
            

System Integration

• Conversational AI: Provides empathetic interaction capabilities to chatbots and voice assistants.
• Feedback Collection: Works alongside ai_feedback_collector.py to analyze emotional responses.
• AI Personality Adjustments: Informs personality adaptation models in ai_personality_module.py.

Future Enhancements

• Enhanced Multimodal Fusion: Incorporate advanced fusion techniques using attention mechanisms.
• Personalized Emotion Modeling: Adapt emotional profiles per user behavior over time.
• Real-Time Feedback Loop: Enable immediate adjustments to emotional states in fast-changing interactions.