EmotiAI Technical Deep Dive: Implementing Emotion Memory in AI

Overview

EmotiAI is an innovative system that gives AI agents human-like emotion memory and learning capabilities. Beyond traditional temporary emotion analysis, it achieves emotion memory, integration, and prediction, enabling the construction of AI agents with true emotional intelligence (EQ).

This article provides a detailed explanation of EmotiAI's core technical components.

1. Hybrid Emotion Model (OCC + Plutchik)

OCC Theory (Ortony, Clore, Collins)

OCC theory views emotions as a cognitive appraisal process. It analyzes emotions from three aspects: events, agents, and objects.

• Event-based: joy, distress, hope, fear
• Agent-based: pride, shame, admiration, reproach
• Object-based: love, hate

Plutchik Theory (Wheel of Emotions)

Plutchik's Wheel of Emotions is a model that visually represents 8 basic emotions and their combinations.

• 8 Basic Emotions: joy, trust, fear, surprise, sadness, disgust, anger, anticipation
• Intensity Levels: 3 levels (mild, moderate, intense) for each emotion
• Emotion Combinations: love (joy + trust), guilt (joy + fear), etc.

Hybrid Model Implementation

EmotiAI implements a hybrid model integrating these two theories:

from backend.src.models.emotion_models import HybridEmotionModel

# Initialize
emotion_model = HybridEmotionModel()

# Analyze emotion from text
text = "I am so happy to see you!"
result = emotion_model.analyze(text)

print(f"Emotion: {result['emotion']}")      # joy
print(f"Intensity: {result['intensity']}")  # 0.8
print(f"Arousal: {result['arousal']}")      # 0.7
print(f"Valence: {result['valence']}")      # 0.9

2. Q-Learning for Emotion-Action Optimization

Q-Learning Algorithm

Q-learning is a type of reinforcement learning that learns the "value (Q-value)" for state-action pairs. EmotiAI uses it to select optimal actions based on emotional states.

Implementation Example

from backend.src.ai import QLearner, ActionSelector

# Initialize Q-learner
q_learner = QLearner(
    learning_rate=0.1,       # Learning rate
    discount_factor=0.95,    # Discount factor
    exploration_rate=0.2     # Exploration rate (ε-greedy)
)

# Action selector
action_selector = ActionSelector(q_learner)

# Select action from emotional state
emotional_state = {"joy": 0.7, "excitement": 0.5}
action = action_selector.select_action(emotional_state)

# Learn from feedback
reward = 1.0  # Positive feedback
q_learner.update(emotional_state, action, reward)

Q-Value Update Formula

Q-values are updated using the following formula:

Q(s, a) ← Q(s, a) + α[r + γ max Q(s', a') - Q(s, a)]

s: Current emotional state
a: Selected action
r: Reward (feedback)
α: Learning rate
γ: Discount factor
s': Next emotional state

3. Emotion Memory System

Three-Layer Memory Structure

EmotiAI's memory system adopts a three-layer structure inspired by human memory models:

1. Short-term memory (Redis): Fast retrieval of recent emotional states
2. Medium-term memory (PostgreSQL): Retains memories for 1 week to 1 month
3. Long-term memory (PostgreSQL integration): Integrated and compressed important memories

Memory Integration Algorithm

Integrates similar emotional memories and stores them as long-term memory:

from backend.src.core import MemoryManager
from backend.src.models import EmotionalMemory

memory_mgr = MemoryManager()

# Store memory
memory = EmotionalMemory(
    emotion="joy",
    intensity=0.8,
    context="Reunion with friends",
    timestamp="2025-11-26T10:00:00Z"
)
memory_mgr.store(memory)

# Retrieve similar memories
similar_memories = memory_mgr.retrieve(
    emotion="joy",
    similarity_threshold=0.7,
    limit=10
)

Memory Decay Model

Memories decay over time, but the decay rate varies with emotional intensity:

def calculate_decay(intensity: float, days_elapsed: int) -> float:
    """
    Calculate memory decay

    Strong emotions (intensity > 0.7) decay slowly
    Weak emotions (intensity < 0.3) decay quickly
    """
    base_decay = 0.95 ** days_elapsed
    intensity_factor = intensity ** 0.5
    return base_decay * intensity_factor

4. 3D Emotion Space Mapping

Three-Dimensional Emotion Space

By mapping emotions to a three-dimensional space (Valence, Arousal, Dominance), we can calculate similarity and transitions between emotions.

• Valence: Positive↔Negative (-1.0 to +1.0)
• Arousal: Calm↔Excited (0.0 to 1.0)
• Dominance: Passive↔Active (0.0 to 1.0)

Implementation Example

from backend.src.core import Emotion3DManager

emotion_3d = Emotion3DManager()

# Map emotion to 3D space
vector = emotion_3d.map_to_3d(
    emotion="joy",
    intensity=0.8,
    arousal=0.7,
    valence=0.9
)

print(f"3D Vector: {vector}")
# Output: [0.9, 0.7, 0.8]

5. Personalization Features

User-Specific Emotion Profiles

EmotiAI creates an emotion profile for each user and provides personalized responses:

• Emotion Tendencies: Which emotions occur frequently
• Reaction Patterns: Emotion responses to specific contexts
• Preferred Actions: Past action history

from backend.src.core import EmotionProfileManager

profile_mgr = EmotionProfileManager()

# Get user's emotion profile
profile = profile_mgr.get_profile(user_id="user_123")

print(f"Most frequent emotion: {profile.most_frequent_emotion}")
print(f"Emotion stability: {profile.stability_score}")

6. NLP Integration

Multilingual Emotion Analysis

EmotiAI supports emotion analysis in multiple languages (Japanese, English, Chinese, Korean):

from backend.src.nlp import MultilingualAnalyzer

analyzer = MultilingualAnalyzer()

texts = {
    "ja": "今日は本当に嬉しい！",
    "en": "I am so happy today!",
    "zh": "今天真的很开心！",
    "ko": "오늘 정말 기뻐요!"
}

for lang, text in texts.items():
    emotion = analyzer.analyze(text, language=lang)
    print(f"{lang}: {emotion}")

7. Architecture

System Components

• Emotion Analysis Engine: Hybrid model (OCC + Plutchik)
• Q-Learning Agent: Action optimization
• Memory Manager: Three-layer memory management
• API Gateway (FastAPI): REST API
• Cache Layer (Redis): High-speed data access
• Database (PostgreSQL): Persistent storage

Technology Stack

8. Performance

Benchmark Results

9. Security & Privacy

Data Encryption

• At Rest: AES-256 encryption for database
• In Transit: TLS 1.3 for all communications

Access Control

• JWT Authentication: Token-based authentication
• Role-Based Access Control: User/admin permissions

GDPR/CCPA Compliance

• Right to Deletion: User memory deletion feature
• Data Portability: Memory data export

Conclusion

EmotiAI provides AI agents with true emotional intelligence through a hybrid emotion model combining OCC and Plutchik theories, Q-learning for action optimization, and a three-layer memory system.

With a high-performance architecture combining Python, FastAPI, Redis, and PostgreSQL, it achieves real-time emotion analysis and long-term memory.

Related Links

• EmotiAI Product Page
• AI Agent Development Services