Cognitive agent abstractions can help to engineer intelligent systems across mobile devices. On smartphones, the data obtained from onboard sensors can give valuable insights into the user's current situation. Unfortunately, today's cognitive agent frameworks cannot cope well with the challenging characteristics of sensor data. Sensor data is located on a low abstraction level and the individual data elements are not meaningful when observed in isolation. In contrast, cognitive agents operate on high-level percepts and lack the means to effectively detect complex spatio-temporal patterns in sequences of multiple percepts. In this paper, we present a stream-based perception approach that enables the agents to perceive meaningful situations in low-level sensor data streams. We present a crowdshipping case study where autonomous, self-interested agents collaborate to deliver parcels to their destinations. We show how situations derived from smartphone sensor data can trigger and guide auctions, which the agents use to reach agreements. Experiments with real smartphone data demonstrate the benefits of stream-based agent perception.

翻译：认知智能体抽象有助于在移动设备上构建智能系统。在智能手机中，机载传感器获取的数据能够提供对用户当前情境的宝贵洞察。然而，当前的认知智能体框架难以有效应对传感器数据的挑战性特征：传感器数据处于低抽象层级，单个数据片段在孤立观察时缺乏意义。相反，认知智能体在处理高层级感知时，缺乏有效检测多感知序列中复杂时空模式的手段。本文提出一种基于流的感知方法，使智能体能够从低层级传感器数据流中感知有意义的情境。我们通过众包物流案例研究展示了该方法——其中自主、自利的智能体协作完成包裹配送任务，并阐明如何利用智能手机传感器数据衍生的情境触发和引导智能体达成协议的拍卖机制。基于真实智能手机数据的实验验证了流式智能体感知方法的优势。