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Spoken Language Interaction With Model Uncertainty: An Adaptive Human-robot Interaction System

机译：具有模型不确定性的口语交互：自适应人机交互系统

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摘要

Spoken language is one of the most intuitive forms of interaction between humans and agents. Unfortunately, agents that interact with people using natural language often experience communication errors and do not correctly understand the user's intentions. Recent systems have successfully used probabilistic models of speech, language and user behaviour to generate robust dialogue performance in the presence of noisy speech recognition and ambiguous language choices, but decisions made using these probabilistic models are still prone to errors owing to the complexity of acquiring and maintaining a complete model of human language and behaviour. In this paper, a decision-theoretic model for human-robot interaction using natural language is described. The algorithm is based on the Partially Observable Markov Decision Process (POMDP), which allows agents to choose actions that are robust not only to uncertainty from noisy or ambiguous speech recognition but also unknown user models. Like most dialogue systems, a POMDP is defined by a large number of parameters that may be difficult to specify a priori from domain knowledge, and learning these parameters from the user may require an unacceptably long training period. An extension to the POMDP model is described that allows the agent to acquire a linguistic model of the user online, including new vocabulary and word choice preferences. The approach not only avoids a training period of constant questioning as the agent leams, but also allows the agent actively to query for additional information when its uncertainty suggests a high risk of mistakes. The approach is demonstrated both in simulation and on a natural language interaction system for a robotic wheelchair application.
机译：口语是人与代理之间互动的最直观形式之一。不幸的是，使用自然语言与人进行交互的代理经常会遇到通信错误，并且无法正确理解用户的意图。最近的系统已经成功地使用语音，语言和用户行为的概率模型在嘈杂的语音识别和模棱两可的语言存在下产生强大的对话性能，但是使用这些概率模型做出的决策由于获取和获取信息的复杂性仍然容易出错。维护人类语言和行为的完整模型。在本文中，描述了一种使用自然语言进行人机交互的决策理论模型。该算法基于部分可观察的马尔可夫决策过程（POMDP），该过程使代理能够选择不仅对噪声或模棱两可的语音识别的不确定性而且对未知的用户模型具有鲁棒性的动作。像大多数对话系统一样，POMDP由可能难以根据领域知识指定先验条件的大量参数定义，并且从用户那里学习这些参数可能需要很长的训练时间。描述了对POMDP模型的扩展，该扩展使代理可以在线获取用户的语言模型，包括新的词汇和单词选择首选项。该方法不仅避免了代理人不断学习的持续训练时间，而且还允许代理人在不确定性表明存在很高的错误风险时主动查询其他信息。该方法已在仿真和用于轮椅轮椅应用的自然语言交互系统中得到了证明。

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