Emergence of Complex Behavior in Multi-Agent Systems

Title: Emergence of Complex Behavior in Multi-Agent Systems

Research Question: How do simple, local interactions among many agents lead to complex, collective behavior?

Methodology: The researchers studied mathematical models that describe the dynamics of collective behavior in multi-agent systems. They focused on Markov or memoryless agents, which rely only on their present state and not past states to determine their future state.

Results: The researchers found that the behavior of an individual agent can be considered stochastic and unpredictable, but the collective behavior of such systems can have a simple probabilistic description. They showed that a class of mathematical models could be written down from the details of the individual agent controller.

Implications: This research suggests that complex behavior can emerge from simple, local interactions among many agents. This finding has significant implications for the design of distributed systems, robotics, and artificial intelligence, as it allows for the creation of systems that are robust, flexible, and scalable. It also provides insights into how simple organisms can exhibit complex, collective behavior in nature.

Link to Article: https://arxiv.org/abs/0404002v1 Authors: arXiv ID: 0404002v1