用佩特里网让机器人根据上下文自适应解释行为，提升人机信任。
Using Petri Nets for Context-Adaptive Robot Explanations
- 用佩特里网建模人机交互中的上下文状态与依赖关系。
- 验证了无死锁、可到达性、有界性和活性等关键性质。
- 适合研究人机交互可解释性与形式化验证的学者。
在人机交互中，机器人需以自然透明的方式沟通以建立信任，这要求其通信能根据上下文动态调整。本文提出使用佩特里网（Petri nets, PNs）建模上下文信息，以实现自适应解释。PNs 提供了一种形式化、图形化的表示方法，可用于描述并发动作、因果依赖及系统状态，特别适用于分析人机之间的动态交互。通过一个机器人基于用户注意力和存在性等上下文线索提供解释的场景，我们展示了该方法的有效性。模型分析证实了关键性质：无死锁、上下文敏感可达性、有界性与活性，表明佩特里网在设计与验证人机交互中的自适应解释机制方面具备鲁棒性与灵活性。
原文摘要 · Abstract (English)
In human-robot interaction, robots must communicate in a natural and transparent manner to foster trust, which requires adapting their communication to the context. In this paper, we propose using Petri nets (PNs) to model contextual information for adaptive robot explanations. PNs provide a formal, graphical method for representing concurrent actions, causal dependencies, and system states, making them suitable for analyzing dynamic interactions between humans and robots. We demonstrate this approach through a scenario involving a robot that provides explanations based on contextual cues such as user attention and presence. Model analysis confirms key properties, including deadlock-freeness, context-sensitive reachability, boundedness, and liveness, showing the robustness and flexibility of PNs for designing and verifying context-adaptive explanations in human-robot interactions.
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