Most current autonomous driving systems rely on single-agent deep learning models or end-to-end neural networks. While ...

Today, multi-agent systems (MAS) have emerged as transformative technologies, driving innovation and efficiency across various industries. Comprising multiple autonomous agents working collaboratively ...

Multi-agent reinforcement learning (MARL) algorithms play an essential role in solving complex decision-making tasks by learning from the interaction data between computerized agents and (simulated) ...

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 ...

Distributing tasks across multi-agent systems requires a modular approach in which development, testing and troubleshooting are streamlined, reducing disruption. As excited as organizations are about ...

LangGraph has been used to create a multi-agent large language model (LLM) coding framework. This framework is designed to automate various software development tasks, including coding, testing, and ...

As AI-assisted coding becomes more common, a new pattern is emerging: multi-agent workflows. A multi-agent workflow refers to using various AI agents in parallel for specific software development life ...