A method of training an artificial intelligence agent's behavior to achieve the highest social approval by assigning rewards to its actions. This set of rewards is used in reinforced learning or any other learning system to modify the learning agent's behavior. The agent will do a set of trial actions that induces a set of social reactions from the intelligent observers. Each reaction will be analyzed by specialized networks to assign a social approval value as a reward to train the machine learning algorithm. The machine learning agent modifies its actions to achieve the highest anticipated social rewards in the future. The agent has a reinforcement learning unit that is trained by the reward value determined by already trained networks in the reward unit. Each set of actions produces reactions that are captured by sensors such as, but not limited to, vision and audio sensors. These reactions—such as facial expression, voice tone, and body language—will be classified as positive or negative and a reward value will be assigned to them. This set of reward values would then be used to train and modify the agent's behavior.
ARTIFICIAL INTELLIGENT AGENT REWARDING METHOD DETERMINED BY SOCIAL INTERACTION WITH INTELLIGENT OBSERVERS