Proactive Clarification Dialogues under Incomplete Multimodal User Evidence: A Comprehensive Framework
DOI:
https://doi.org/10.66238/fsrma137Keywords:
Proactive Clarification, Multimodal Interaction, Incomplete Evidence, Conversational AgentsAbstract
The rapid evolution of artificial intelligence has facilitated the development of sophisticated multimodal conversational agents capable of processing diverse input streams, including speech, gesture, gaze, and physiological signals. However, real-world deployment of these systems is frequently hindered by the presence of incomplete, ambiguous, or degraded multimodal user evidence. Traditional dialogue systems often adopt a reactive posture, proceeding with erroneous assumptions or generating generic error prompts when faced with uncertainty. This paper proposes a comprehensive framework for proactive clarification dialogues, enabling agents to preemptively identify missing information and solicit targeted clarifications before executing potentially irreversible actions. By systematically categorizing sources of multimodal incompleteness and establishing a decision-theoretic threshold for intervention, the proposed methodology balances task efficiency with conversational naturalness. Through extensive simulation in a virtual physical environment, this study evaluates the efficacy of proactive clarification against traditional reactive baselines. The findings indicate that proactive systems significantly enhance task success rates and user satisfaction, particularly in environments characterized by high sensor noise and occlusion. This research contributes to the broader domain of human-computer interaction by providing a robust theoretical and practical foundation for designing more resilient, user-centric conversational agents capable of navigating the inherent unpredictability of human communication.
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