A learning interface agent for scheduling meetings
IUI '93 Proceedings of the 1st international conference on Intelligent user interfaces
Agents that reduce work and information overload
Communications of the ACM
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Using explicit requirements and metrics for interface agent user model correction
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Principles of mixed-initiative user interfaces
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Intelligent Systems
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Utility Theory-Based User Models for Intelligent Interface Agents
AI '98 Proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
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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.