Agent-based modeling (ABM) in AI is a powerful computational approach that simulates the actions and interactions of autonomous agents within an environment. Each agent represents an individual entity, such as people, organizations, or even biological cells, endowed with its own rules, behaviors, and decision-making capabilities. Instead of defining global system behavior explicitly, ABM focuses on how complex system-level patterns and emergent behaviors arise from these simple local interactions among agents. This bottom-up methodology allows researchers to explore dynamic processes, predict outcomes, and understand how individual choices propagate throughout a system. Crucially, AI techniques often power the agents' intelligence, enabling them to learn, adapt, and make sophisticated decisions, making ABM a valuable tool for studying complex adaptive systems in fields ranging from economics to ecology. More details: https://info-ai.top