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Give AI agents human-like emotion memory and learning capabilities. Achieve true emotional experiences in retail, gaming, and metaverse.
Integrated into OmniCreator - Learn about AI Agent DevelopmentAnalyze customer facial expressions and behavior from store cameras/sensors. Remember emotion changes and provide personalized service on next visit.
NPCs and training characters remember and learn emotions. Personality changes based on player actions, providing immersive gaming experiences.
Virtual avatars with emotion expression and memory for realistic interactions. Analyze audience emotions at live events and dynamically adjust performances.
Unique emotion engine combining OCC + Plutchik theories with Q-learning
EmotiAI's emotional intelligence system is integrated into OmniCreator's 850+ specialized AI agents, giving each agent the ability to understand, remember, and learn emotions. This enables more human-like and context-aware responses.
Integrate OCC theory (emotions based on cognitive appraisal) with Plutchik theory (wheel of basic emotions). Accurately model complex emotional states.
Optimize action selection based on emotional state with reinforcement learning. Achieve natural responses with high-speed processing within 10ms.
Integrate short-term and long-term memory management. Automate memory decay, consolidation, and prediction to forecast future from past emotions.
Support multiple languages including Japanese, English, Chinese, Korean. Extract emotions from text while considering cultural context.
Store recent emotions in fast-access memory. Achieve real-time responses within 10ms.
Persist important emotional experiences. Memory decay algorithm reproduces natural forgetting, memory consolidation groups related emotions.
Predict future emotional states from past patterns. Detect potential dissatisfaction like "this customer may be unhappy next time" in advance.
Learn optimal actions for each emotional state. Auto-select context-appropriate responses like "anger → apologize" or "joy → additional suggestions."
Learn from action results as feedback. Increase value of successful actions, adjust to avoid failed actions.
Learn environmental context like time, location, situation. Optimize for environment like "evening fatigue → brief interaction."
Analyze customer facial expressions with cameras. Alert staff immediately if dissatisfied, suggest add-ons if satisfied. Remember emotion history for next visit.
Automatically identify premium customers from past emotion history. Notify special treatment on arrival to improve satisfaction.
NPCs remember player choices and actions. Become attached if treated kindly, keep distance if treated coldly. Provide immersive experiences.
Personality changes based on training method. Strict training breeds courage, gentle training breeds mildness. Create truly unique characters.
Avatars remember past conversations and respond emotionally. Recreate realistic human relationships in virtual spaces.
Analyze audience emotions in real-time. Enhance lighting/sound if excited, adjust tempo if fatigue detected.
Not just one-time interactions. Remember emotions and leverage them next time for long-term relationship building.
Optimize action selection based on emotional state with Q-learning. Achieve more natural AI responses.
Integrate OCC + Plutchik theories. Accurately analyze and predict complex human emotions.
EmotiAI is an emotional intelligence engine that gives AI agents human-like emotion memory and learning capabilities. By combining a hybrid emotion model integrating OCC and Plutchik theories with Q-learning algorithms, it enables AI to understand, remember, and learn emotions.
EmotiAI can be used across various industries including retail/stores, game development, and metaverse/live entertainment. In retail, optimize customer service through emotion analysis; in games, create immersive experiences with emotionally growing NPCs; in metaverse, enable realistic interactions through avatar emotion expression.
OCC theory is an emotion generation model based on cognitive appraisal, deriving emotions from evaluations of events and others' actions. Plutchik theory expresses 8 basic emotions (joy, trust, fear, surprise, sadness, disgust, anger, anticipation) and complex emotions through their combinations. EmotiAI integrates these two theories for more accurate emotion modeling.
Q-learning is a reinforcement learning algorithm used in EmotiAI for optimal action selection based on emotional states. AI learns from action results as feedback, enabling automatic selection of optimal responses for situations like "anger → apologize" or "joy → additional suggestions."
EmotiAI's emotional intelligence system is integrated into OmniCreator's 850+ specialized AI agents. This gives each agent the ability to understand, remember, and learn emotions, enabling more human-like and context-aware responses.
It varies depending on project scale and requirements, but typically 2-4 weeks for standard API integration, and 1-3 months for customized solutions. You can see actual operation through a free demo.
See how EmotiAI can bring emotional experiences to your business with a free demo.