---
title: "Sustainable Intelligence for the Wild: Democratizing Ecological Monitoring via Knowledge-Adaptive Edge Expert Agents"
authors:
  - "Jiaxing Li"
  - "Hao Fang"
  - "Chi Xu"
  - "Miao Zhang"
  - "Jiangchuan Liu"
  - "William I. Atlas"
  - "Katrina M. Connors"
  - "Mark A. Spoljaric"
doi: "10.24963/ijcai.2026/811"
url: "https://ijcai.org/proceedings/2026/811"
pdf: "https://ijcai.org/proceedings/2026/0811.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "7292-7300"
track: "AI and Social Good"
keywords:
  - "Humans and AI: Humans and AI"
  - "Multidisciplinary Topics and Applications: Multidisciplinary Topics and Applications"
  - "Knowledge Representation and Reasoning: Knowledge Representation and Reasoning"
  - "Agent-based and Multi-agent Systems: Agent-based and Multi-agent Systems"
---

# Sustainable Intelligence for the Wild: Democratizing Ecological Monitoring via Knowledge-Adaptive Edge Expert Agents

Jiaxing Li, Hao Fang, Chi Xu, Miao Zhang, Jiangchuan Liu, William I. Atlas, Katrina M. Connors, Mark A. Spoljaric

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 7292-7300. DOI: [10.24963/ijcai.2026/811](https://doi.org/10.24963/ijcai.2026/811)

## Abstract

Rapid biodiversity loss underscore the urgency of effective monitoring, yet manual surveys remain resource-intensive. While on-device AI offers a scalable alternative, its performance in the wild is often challenged by environmental variability. Current methods rely heavily on cloud resource, which requires continuous uploading of field data for model retraining. This approach is unsuitable for remote deployments because it consumes limited power and network connectivity. To address these constraints, this research proposes a shift from model adaptation to knowledge adaptation. We introduce an architecture that separates visual perception from reasoning, combining a visual encoder with a dynamic knowledge base. We uses an explicit knowledge base to replace implicitly encoding expert knowledge into model parameters. This method also supports knowledge sustainability by preserving expert insights in a structured form. Through cross-disciplinary collaboration with biologists and Indigenous communities, this work advances ethical AI co-development, fostering responsible and culturally informed ecosystem management.