Framework helps interpret user intents and find cloud services despite conflicts
A Negotiation-Aware Hybrid LLM Framework for Intent Interpretation and Service Discovery
Artificial IntelligenceSoftware Engineering
Summary
People often describe what they want from cloud services in natural language, which can be unclear or contradictory. The authors propose a system that can understand these intent statements, identify any conflicts, and then find appropriate cloud services that match the user’s needs. If conflicts or unsatisfiable requests arise, the system negotiates with the user in natural language to clarify and resolve them. This approach improves how well the system detects conflicts and finds suitable services compared to simpler existing methods.
What this means in practice
- •For cloud service engineers: interpret vague or conflicting user requests into structured service definitions to improve cloud service composition accuracy
- •For enterprise it teams: discover and select cloud service offers that best match high-level business intents even when requirements conflict or are incomplete
- •For automated service brokers: enable natural-language negotiation to resolve conflicting or unsatisfiable service requirements during cloud service discovery$Commercial implications: This paper enables products that interact with users in natural language to refine service requests and select cloud offerings automatically.
Authors
Wided Meflah, Hayet Brabra, Walid Gaaloul
Abstract
Increasingly, users express their needs as natural-language intents rather than structured requests. These intents may be incomplete, high-level, or internally conflicting. A conflicting intent either violates fundamental properties of the cloud paradigm or states mutually inconsistent requirements. Classical composition approaches are designed for structured, low-level, and complete requests. They cannot interpret such intents, discover suitable services from provider catalogs, or negotiate conflicting requirements with users. To address this gap, we propose a negotiation-aware framework for intent interpretation and service discovery. The framework first refines the natural-language intent and dynamically detects conflicts. It then structures the intent into a provider-independent TOSCA template. For each specification, it retrieves candidate service offers through deterministic matching grounded in cloud provider offerings. Some conflicts arise during interpretation. Other requirements prove unsatisfiable during discovery. Both are resolved through iterative natural-language negotiation with the user. We evaluate the framework on 70 diverse intents against single-shot and enhanced-prompting baselines. It improves recall and F1 for both conflict detection and service discovery. It also eliminates hallucinated offers and resolves conflicts at a cost proportional to their number.