A multi-agent system (MAS) is a type of computerized system comprised of multiple intelligent agents working in tandem to solve complex problems. These agents, varying in autonomy and objectives, operate within diverse environments that can be virtual, discrete, or continuous. MAS are characterized by autonomy, local perspectives, and decentralization, and find applications in areas such as gaming, networking, transportation, and social simulations for research.
Delving into the effectiveness of a multi-agent AI approach in addressing intricate challenges, this article discusses the innovative work of Tom Zahavy, a computer scientist and research scientist at Google DeepMind. Zahavy developed an advanced system that integrates up to 10 different AI decision-making systems, each trained in unique strategies. This integrated approach outperformed AlphaZero when tasked with solving Penrose’s puzzles. The system’s ability to seamlessly switch between strategies when encountering obstacles highlights its superior performance and innovative problem-solving capabilities.
Zahavy’s work underscores the advantages of employing diverse strategies and fostering internal collaboration within AI systems. It also sheds light on the limitations of deep reinforcement learning, a technique utilized in formidable AI applications such as AlphaZero and self-driving cars. Although powerful, deep reinforcement learning often struggles with generalization and adaptation to new or unforeseen challenges.
In the realm of chess, the diversified AI approach not only solved a greater number of puzzles but also exhibited a richer array of strategies during gameplay. This methodology holds promise for wide-ranging applications, including robotics, drug discovery, and stock trading, offering innovative and efficient solutions. The article proposes that AI intelligence and creativity might stem from the computational capacity to assess and select from a plethora of options. This notion reinforces the idea that creativity can be viewed as a computational problem.
The article concludes by addressing the practical hurdles and potential enhancements of this approach, such as computational costs and the pursuit of even more diverse solutions. Reflecting Zahavy’s belief in the existence of multiple solutions to complex problems, these considerations underscore the ongoing evolution and improvement of multi-agent AI systems.