Intelligence for Orchestration

Agents for automatic configuration synthesis in view of
- Ensuring proactive real-time resource management
- Ensuring multi-objective resource allocation
- Ensure error-free configuration and orchestration

Enabled through structured reasoning, exact optimization guided by supervised learning, multi-agent reinforcement learning, AI planning agents.

Selected relevant publications:

1) An Overview and Solution for Democratizing AI Workflows at the Network Edge, A. Čop, B. Bertalanič, C. Fortuna, Journal of Network and Computer Applications, 2025
2) Multi-Agent Reinforcement Learning-based In-place Scaling Engine, J. Prodanov, B. Bertalanic, …., J. Hribar, To appear
3) Analysis of AI Planning Techniques for Resource Orchestration, S. Gojayev, A. Anaqreh, C, Fortuna, To appear.
4) Agent based Resource Orchestration with Hybrid Large Language Models, S. Gojayev, A. Anaqreh, C, Fortuna, To appear.
5) The Energy Cost of Artificial Intelligence, S.-K. Chou, J. Hribar, V. Hanžel, M. Mohorčič, C. Fortuna, To appear , current unrevised version: https://arxiv.org/abs/2408.00540v2
6) Resource-Aware Time Series Imaging Classification for Wireless Link Layer Anomalies, B. Bertalanič, M. Meža, C. Fortuna, IEEE Transactions on Neural Networks and Learning Systems, 2023
7) HANNA: Human-Friendly Provisioning and Configuration of Smart Devices, C. Fortuna, H. Yetgin, L. Ogrizek, E. Municio, J. M. Marquez-Barja, M. Mohorčič, Engineering Applications of Artificial Intelligence, 2023
8) Smart Infrastructures: Artificial Intelligence-Enabled Lifecycle Automation, C. Fortuna, H. Yetgin, M. Mohorčič, IEEE Industrial Electronics Magazine, 2023