Intent Representation and Formalization for Autonomous Human–Internet Interaction: A Unified Model of Goals, Constraints, Preferences, and Expected Outcomes

The development of generative artificial intelligence, autonomous agents, intelligent information retrieval, and cross-platform digital services is creating new possibilities for human–internet interaction. Users can increasingly express complex objectives through natural language and expect intelligent systems to coordinate the digital capabilities required to accomplish them. However, a fundamental problem remains insufficiently resolved: how should a computational system represent the underlying intention expressed by a human user in a form that is sufficiently structured, precise, and adaptable to support reliable autonomous execution? Natural-language requests frequently combine multiple semantic components, including goals, constraints, preferences, contextual assumptions, implicit requirements, and expected outcomes. These components are not interchangeable. A mandatory budget limitation, for example, has a fundamentally different operational meaning from a preference for a particular hardware manufacturer. Similarly, the user's requested outcome is not necessarily equivalent to the linguistic content of the request. Systems that represent intent primarily through single-label classification, keywords, or flat semantic frames may therefore lose information necessary for complex task execution. This paper proposes Intent Representation and Formalization (IRF), a foundational research framework for transforming natural-language user requests into structured, machine-interpretable intent representations. Its central conceptual contribution is the Unified Intent Representation Model (UIRM), which organizes intent into interconnected dimensions covering objectives, goal hierarchies, constraints, preferences, contextual conditions, expected outcomes, execution requirements, uncertainty, and temporal evolution. The proposed model introduces several mechanisms for intent formalization, including hierarchical goal decomposition, explicit–implicit intent separation, hard–soft constraint differentiation, preference representation, constraint conflict detection, intent incompleteness assessment, uncertainty-aware interpretation, outcome-contract specification, provenance-aware intent tracking, and dynamic intent revision. It further distinguishes the semantic representation of what a user wants from the execution plan through which a system attempts to achieve it. The paper develops a mathematical representation, a formal intent-state model, a constraint-resolution mechanism, an intent-update model, and an algorithmic workflow for converting natural-language requests into structured intent objects. It also establishes the conceptual relationship between IRF, Intent-Driven Interaction (IDI), Intent-Driven Capability Orchestration (IDCO), and the author's previously proposed Generative AI and Search Engine Orchestration (GASEO) framework. Rather than claiming that structured intent representation, goal modeling, semantic parsing, or autonomous planning are new research areas, this study positions its contribution in their integration into a unified, user-centered intent representation framework intended to bridge natural-language objectives and autonomous digital execution. The framework is conceptual and does not claim completed experimental validation or demonstrated performance superiority. It establishes a theoretical foundation and a systematic research agenda for future implementation and empirical evaluation.