# Stream-based perception for cognitive agents in mobile ecosystems

arXiv: https://arxiv.org/abs/2401.13604 · HTML: https://mlsanity.com/paper/2401.13604/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 54 references that are in the map.
- First posted on arXiv 24 Jan 2024.

## Abstract (arXiv)

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.