Multi-Agent Reinforcement Learning (MARL) is an emerging subfield of artificial intelligence that investigates how multiple autonomous agents can learn collaboratively and competitively within an ...

This collection supports and amplifies research related to SDG 4: Quality Education. Generative AI is transforming the conventional dyadic teacher-student dynamic into a triadic framework centered ...

Kimi K2.5 introduces a multi-agent orchestration with up to 100 workers, helping teams cut complex task time and boost accuracy.

Welcome to the world of RDHNet, a groundbreaking approach to multi-agent reinforcement learning (MARL) introduced by Dongzi Wang and colleagues from ...

The overall relationship between the attacker and the ego system. The black solid arrows indicate the direction of data flow, the red solid ones indicate the direction of gradient flow and the red ...

Large language models like ChatGPT play an ever-evolving role in the modern business landscape. Your curiosity may have led you to engage two AI models in conversation before, but have you considered ...

The biggest challenge to AI initiatives is the data they rely on. More powerful computing and higher-capacity storage at lower cost has created a flood of information, and not all of it is clean. It ...

Artur Schweidtmann says multi-agent systems can reshape the way engineers design and operate chemical plants – turning AI into collaborative digital teammates rather than replacements ...

The governance challenge is intensifying as digital systems increasingly optimize for machine consumption rather than human ...

Varun is a Product management and AI leader, shaping the future of tech with strategic vision, AI platforms and agentic-AI experiences. Three weeks ago, I witnessed AI agents solving a complex ...