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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.15818740 |
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Table of Contents:
- <p><span>Dialogue management systems serve as the decision-making core of conversational agents, enabling them to maintain coherent interactions across multiple turns. This article explores the fundamental components, architectural approaches, and implementation strategies that allow conversational systems to determine appropriate responses in dynamic user interactions. Beginning with an examination of core mechanisms, including dialogue state tracking and policy selection, the discussion progresses through various implementation paradigms ranging from rule-based systems to sophisticated machine learning architectures. Context management strategies receive particular attention as critical enablers of natural conversation flow, with techniques such as slot-filling, frame-based representation, and hierarchical goal structures shown to significantly enhance user experience. Real-world applications at the end of the exploration show how dialogue management makes possible features like proactive suggestion creation, multi-turn interactions, environmental adaption, educational tutoring, personalisation, and ambiguity resolution. Throughout, the article highlights how effective dialogue management transforms fragmented exchanges into coherent conversations, creating experiences that increasingly mirror the contextual awareness and adaptability of human interaction while addressing practical implementation challenges faced by organizations deploying these technologies.</span></p>