Based on massive data, AI algorithms are changing the operation mode of traditional industries at an unprecedented speed and in an unimaginable way, creating new driving forces for the development of all walks of life, and accelerating intelligent and high-quality economic and social development.

Multiple application scenarios

Algorithms, which can be regarded as "information assistants," help us to efficiently distribute, process, analyze, and tap into the value of vast amounts of data.

The deep integration of algorithms and scenarios is being widely used in various industries, changing the operating modes of traditional industries.

An intelligent inspection robot monitoring a hot coking furnace at a steel plant is no longer a scene in a science fiction movie but a real portrayal of intelligent manufacturing today.

Thanks to the environment perception algorithm and decision algorithm, the complex environment intelligent inspection robot can complete tasks in harsh environments such as high temperature, high pressure and explosive.

Today, through real-time data collection and analysis, robots are able to detect potential risks in time and prevent accidents, which not only improves industrial safety but also improves production efficiency.

In the vast ocean of data, algorithms are like a powerful engine, creating new momentum for the development of industries.

Applicability gets improved

In September, a research team from Peking University published a paper on large-scale multi-agent systems in the Nature Machine Intelligence magazine. This was the first time that Chinese researchers achieved efficient decentralized collaborative decision-making in large-scale multi-agent systems, which is conducive to improving the scalability and applicability of AI decision algorithms.

Decentralized multi-agent reinforcement learning has become a research hotspot in international academic circles. It seeks to explore an algorithm that can extend the decision-making ability to complex real systems containing a large number of agents under the condition of limited data and resources.

For example, in a drone formation, each drone is controlled by AI, and we call the controller of each aircraft an agent. This drone formation is composed of multiple agents, which makes it a multi-agent system.

Decentralized multi-agent reinforcement learning enables each agent to realize efficient decentralized collaborative decision-making in a way that does not rely on global information, showing unique advantages.

"This research result is of great value for extending AI models to large-scale multi-agent systems such as large power networks and urban traffic signal control," Ma Chengdong, the first author of the paper, said.