Dynamic Intent Evolution in Human–Internet Interaction: Modeling Changing Goals, Contexts, and Preferences During Autonomous Execution

The development of autonomous Internet systems and intent-driven interaction introduces a fundamental challenge that extends beyond the initial interpretation of user requests: human intentions are not necessarily fixed at the beginning of an interaction. In practical situations, users frequently discover additional requirements, revise their preferences, introduce new constraints, change priorities, or reconsider their objectives while an autonomous system is already executing a task. Consequently, an interaction model that treats the initial user request as a complete and permanent specification may become increasingly inconsistent with the user's actual intentions as execution progresses. This paper proposes Dynamic Intent Evolution (DIE) as a conceptual framework for modeling the continuous development of human intentions during autonomous human–Internet interaction. The framework considers user intent as an evolving computational state rather than a static input. It introduces a structured representation of goals, contextual information, preferences, constraints, priorities, and expected outcomes, together with mechanisms for intent evolution, intent versioning, contextual intent memory, preference adaptation, constraint revision, intent conflict resolution, and long-term goal consistency. The central objective is to enable autonomous Internet systems to maintain alignment with the user's evolving objectives throughout an extended interaction and execution process. Rather than repeatedly interpreting each new user message as an independent task, the proposed framework integrates new information into an existing intent state, determines which components should be preserved, modified, added, suspended, or removed, and evaluates the consequences of these changes for ongoing execution plans. A formal model is developed to describe the relationship between successive intent states, contextual observations, user feedback, and execution adjustments. The paper further introduces a version-controlled intent representation, a multi-dimensional intent revision mechanism, an intent conflict resolution model, and a goal consistency mechanism designed to prevent local preference changes from unintentionally undermining the user's broader objectives. An important distinction is made between explicit user-directed modifications, context-dependent adaptations, and system-inferred changes. This distinction is intended to prevent autonomous systems from treating uncertain behavioral signals as confirmed changes in user preferences. The framework also considers the need for user confirmation when revisions involve irreversible actions, significant financial commitments, sensitive information, or substantial changes to the original objective. Building upon the author's previously proposed Intent-Driven Interaction (IDI) paradigm, this work extends the interaction model from the initial interpretation and orchestration of user intent toward the continuous maintenance and evolution of intent throughout autonomous execution. It also provides a conceptual foundation for integrating dynamic intent management with Generative AI and Search Engine Orchestration (GASEO), autonomous Internet task decomposition, and broader capability-oriented computing systems. This paper presents a conceptual research framework and does not claim completed experimental validation or demonstrated performance superiority. It establishes research propositions, potential advantages, evaluation dimensions, and implementation challenges for future investigation.