The paper discusses the principles of mixed-initiative user interfaces (UIs), which combine automated services with direct manipulation to enhance human-computer interaction. Eric Horvitz, from Microsoft Research, outlines key challenges and opportunities in designing such interfaces. The paper emphasizes the need to balance automation with direct user control, avoiding over-reliance on either approach. It highlights the importance of considering uncertainty about user goals, timing of actions, and the costs and benefits of automated decisions. Principles include providing value-added automation, managing uncertainty, considering user attention, and allowing efficient invocation and termination of services. The LookOut system is presented as a testbed for mixed-initiative UIs, focusing on scheduling and calendar management. It analyzes email messages to infer user goals and automatically schedules appointments. LookOut uses probabilistic methods to determine the likelihood of a user wanting to schedule an event, and it employs a decision-theoretic framework to decide whether to act or ask the user for clarification. The system supports multiple interaction modalities, including manual, automated-assistance, and social-agent modes, allowing users to interact through speech, text, or visual interfaces. LookOut also incorporates mechanisms for lifelong learning, continuously improving its understanding of user goals and attention patterns. The system uses Bayesian methods and machine learning to refine its predictions over time. The paper concludes that integrating automated reasoning with direct manipulation can lead to more effective and intuitive user interfaces, and that continued research in this area will likely yield significant improvements in human-computer interaction.The paper discusses the principles of mixed-initiative user interfaces (UIs), which combine automated services with direct manipulation to enhance human-computer interaction. Eric Horvitz, from Microsoft Research, outlines key challenges and opportunities in designing such interfaces. The paper emphasizes the need to balance automation with direct user control, avoiding over-reliance on either approach. It highlights the importance of considering uncertainty about user goals, timing of actions, and the costs and benefits of automated decisions. Principles include providing value-added automation, managing uncertainty, considering user attention, and allowing efficient invocation and termination of services. The LookOut system is presented as a testbed for mixed-initiative UIs, focusing on scheduling and calendar management. It analyzes email messages to infer user goals and automatically schedules appointments. LookOut uses probabilistic methods to determine the likelihood of a user wanting to schedule an event, and it employs a decision-theoretic framework to decide whether to act or ask the user for clarification. The system supports multiple interaction modalities, including manual, automated-assistance, and social-agent modes, allowing users to interact through speech, text, or visual interfaces. LookOut also incorporates mechanisms for lifelong learning, continuously improving its understanding of user goals and attention patterns. The system uses Bayesian methods and machine learning to refine its predictions over time. The paper concludes that integrating automated reasoning with direct manipulation can lead to more effective and intuitive user interfaces, and that continued research in this area will likely yield significant improvements in human-computer interaction.