  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.
