Multi Agent Search

Multi-agent search explores how multiple autonomous agents can collaboratively or competitively search a space, whether physical (e.g., searching for a lost person or fighting a forest fire) or abstract (e.g., optimizing hyperparameters in machine learning or finding solutions in complex problem landscapes). Research focuses on developing efficient search strategies, often employing evolutionary algorithms, swarm intelligence, or cooperative/competitive game-theoretic models to optimize search performance and guarantee detection. These advancements have implications across diverse fields, improving efficiency in tasks ranging from resource allocation and disaster response to data analysis and machine learning model training.