| Item Type |
Conference or Workshop Item
(Paper)
|
| Abstract |
Low-power sensors are becoming ever more powerful, increasing both their energy efficiency as well as their processing capabilities. Much work in recent years has focused on optimizing machine learning models to low-power systems, typically to locally process sensor data. Significantly less attention has been paid to other artificial intelligence fields such as knowledge representation and automated reasoning, which may contribute to building autonomous devices. In this work, we present a low-power sensor node with an autonomous belief-desire-intention agent. This kind of agent simplifies the implementation of both proactive and reactive behaviors, promoting autonomy in our target applications. It does so by locally perceiving and reasoning, and then wirelessly broadcasting an intention, which can be forwarded to an actuator. The capabilities of the autonomous agent are demonstrated with a light-control application. Experiments demonstrate the feasibility of running intelligent agents in low-power platforms with little overhead. |
| Authors |
William, Jannik; Muller dos Santos, Matuzalém; de Brito, Maiquel; Hübner, Jomi Fred; Vachtsevanou, Danai & Gomez, Andres |
| Language |
English |
| Subjects |
computer science |
| HSG Classification |
contribution to scientific community |
| Date |
2022 |
| Event Title |
Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things (AIChallengeIoT) |
| Event Location |
Boston, United States |
| Event Dates |
6 November 2022 |
| Depositing User |
Danai Vachtsevanou
|
| Date Deposited |
21 Nov 2022 14:35 |
| Last Modified |
21 Nov 2022 14:35 |
| URI: |
https://www.alexandria.unisg.ch/publications/268007 |
Increasing the Intelligence of low-power Sensors with Autonomous Agents
![[img]](http://www.alexandria.unisg.ch/style/images/fileicons/oa_orange.png)
