发表机构
Politecnico di Milano; University of Cambridge（米兰理工大学; 剑桥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
HiBRIDGE提出分层贝叶斯神经网络框架，用于群体-机器人对话管理，通过结构化多阶段决策和不确定性感知，提升预测性能与行为解释的可理解性。
AI 中文摘要
在多参与者人机交互中，机器人必须持续决定对谁发言以及说什么，以便有效参与对话。在真实世界的交互中，这具有挑战性，因为多种行为可能同时合理：机器人可能继续与某位参与者讨论一个话题，通过提问让另一位参与者参与进来，或者面向整个群体发言，而合适的选择既取决于其发言对象，也取决于交互情境。当前方法在表示多种行为合理时的不确定性，以及将决策结构化为语义上有意义的中间步骤以使机器人决策更易于解释方面，仍然存在局限。针对这些问题，我们提出了HiBRIDGE，一种用于群体-机器人对话管理的分层贝叶斯神经网络框架。其贝叶斯公式能够实现不确定性感知的预测，并从有限的交互数据中进行稳健学习，而分层方法则将行为选择表述为一个结构化的多阶段决策过程。我们进一步使用决策树替代模型来研究这种结构是否能够支持更可解释的解释。在三个离线的群体-HRI数据集上，我们的研究结果表明，贝叶斯公式优于其确定性对应方法以及若干最先进的基线方法。接下来，通过一项在线研究（N=20），我们表明，从分层模型导出的解释在理解机器人行为方面被评为更有帮助，并且比从平面模型导出的解释更受青睐。最后，通过我们的面对面研究（N=12），我们证明了HiBRIDGE在自主实时群体交互中的可行性，分层和平面贝叶斯变体均获得积极评价。总体而言，HiBRIDGE将强大的预测性能与结构化的决策过程相结合，支持对机器人行为进行更可解释的解释。
英文摘要
In multi-party human-robot interaction, a robot must continuously decide whom to address and what to say to participate effectively in the conversation. In real-world interactions, this is challenging because several behaviours may be plausible at the same time: a robot might continue a topic with one participant, involve another through a question, or address the whole group, with the appropriate choice depending on both whom it addresses and the interaction context. Current approaches remain limited in representing uncertainty when several behaviours are plausible and in structuring decisions into semantically meaningful intermediate steps that make robot decisions easier to interpret. Addressing these, we present HiBRIDGE, a hierarchical Bayesian neural network framework for group-robot dialogue management. Its Bayesian formulation enables uncertainty-aware prediction and robust learning from limited interaction data, while the hierarchical approach formulates behaviour selection as a structured, multi-stage decision process. We further use decision-tree surrogates to investigate whether this structure can support more interpretable explanations. Across three offline group-HRI datasets, our findings show that Bayesian formulations outperform their deterministic counterparts and several state-of-the-art baselines. Next, through an online study (N=20), we show that explanations derived from the hierarchical model are rated as more helpful for understanding robot behaviour and are preferred over those derived from the flat model. Finally, through our in-person study (N=12), we demonstrate the feasibility of HiBRIDGE for autonomous real-time group interaction, with both hierarchical and flat Bayesian variants positively perceived. Overall, HiBRIDGE combines strong predictive performance with a structured decision process that supports more interpretable explanations of robot behaviour.
Comments28 pages, 8 figures
