Personalizing user-agent interaction

Abstract

Interface agents are computer programs that provide personalized assistance to users with their computer-based tasks. The interface agents developed so far have focused their attention on learning a user´s preferences in a given application domain and on assisting him according to them. However, in order to personalize the interaction with users, interface agents should also learn how to best interact with each user and how to provide them assistance of the right sort at the right time. To fulfil this goal, an interface agent has to discover when the user wants a suggestion to solve a problem or deal with a given situation, when he requires only a warning about it and when he does not need any assistance at all. In this work, we propose a learning algorithm, named WoS, to tackle this problem. Our algorithm is based on the observation of a user´s actions and on a user´s reactions to the agent´s assistance actions. The WoS algorithm enables an interface agent to adapt its behavior and its interaction with a user to the user´s assistance requirements in each particular context.