Agents that reduce work and information overload
Communications of the ACM
The structure of hypertext activity
Proceedings of the the seventh ACM conference on Hypertext
A study of navigational support provided by two World Wide Web browsing applications
Proceedings of the the seventh ACM conference on Hypertext
Learning personal preferences on online newspaper articles from user behaviors
Selected papers from the sixth international conference on World Wide Web
The Challenges of Mobile Computing
Computer
AI and navigation on the Internet and Intranet
IEEE Expert: Intelligent Systems and Their Applications
AI on the WWW: Supply and Demand Agents
IEEE Expert: Intelligent Systems and Their Applications
Is it an Agent, or Just a Program?: A Taxonomy for Autonomous Agents
ECAI '96 Proceedings of the Workshop on Intelligent Agents III, Agent Theories, Architectures, and Languages
Learning from Hotlists and Coldlists: Towards a WWW Information Filtering and Seeking Agent
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
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Many problems frustrate WWW users, who suffer limited accessibility to WWW resources, and so the authors propose a two-level agent architecture to try to solve them. The agent consists of two parts: the local agent interacts directly with end-users, while the remote agent interacts primarily with the public Internet. Together they learn user preferences, and help users find and filter information more efficiently, economically, and enjoyably. The paper introduces the concept of the two-level agent, and investigates practical issues involved in further design.