Dual Neural Network Architecture for Personalisation in HCI
As digital systems pervade almost all domains of everyday human life, it is becoming desirable for them to offer dynamic behavior. Such change is required in two ways: first, the system should be able to adapt its general behavior based on previous experiences to arrive at better solutions, and it should also personalize to its users’ needs and preferences.
New reinforcement learning methods and approaches that support system adaptability will be described. A two-part neural network architecture will be presented that enables intelligent agents to acquire a general behavior and personalize it directly form interactions. This approach supports generalization of experiences with homogenous environments, while a single trained agent provides optimal policies personalized to each environment.
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