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Paper Number
ICIS2025-1241
Paper Type
Short
Abstract
Autonomous web agents are increasingly capable of completing real-world tasks, yet they struggle to stay aligned with evolving user intent and dynamic web layouts. We present Web2HMM, a Hidden Markov Model-based framework that enables real-time human-in-the-loop interaction during agent execution. By inferring latent decision states, AUTO for confident actions and PAUSE for seeking user input, the agent dynamically balances autonomous operation with timely, proactive feedback. Applied to simulated hotel booking scenarios, Web2HMM demonstrates more adaptive and user-aware behavior, advancing the design of collaborative and resilient web agents.
Recommended Citation
Sun, Yan and Kok, Stanley, "Toward Interactive Web Agents: Proactive Feedback through Hidden Markov-Based Decision Modeling" (2025). ICIS 2025 Proceedings. 7.
https://aisel.aisnet.org/icis2025/hti/hti/7
Toward Interactive Web Agents: Proactive Feedback through Hidden Markov-Based Decision Modeling
Autonomous web agents are increasingly capable of completing real-world tasks, yet they struggle to stay aligned with evolving user intent and dynamic web layouts. We present Web2HMM, a Hidden Markov Model-based framework that enables real-time human-in-the-loop interaction during agent execution. By inferring latent decision states, AUTO for confident actions and PAUSE for seeking user input, the agent dynamically balances autonomous operation with timely, proactive feedback. Applied to simulated hotel booking scenarios, Web2HMM demonstrates more adaptive and user-aware behavior, advancing the design of collaborative and resilient web agents.
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