Stream-based perception for cognitive agents in mobile ecosystems
This work addresses the problem of integrating sensor data into cognitive agent frameworks for mobile ecosystems, representing an incremental improvement in agent perception capabilities.
The paper tackles the challenge of cognitive agents processing low-level sensor data by introducing a stream-based perception approach, which enables agents to detect meaningful situations in sensor streams and is demonstrated in a crowdshipping case study with real smartphone data.
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.