  Many robotic applications, such as search-and-rescue, require multiple agents
to search for and perform actions on targets. However, such missions present
several challenges, including cooperative exploration, task selection and
allocation, time limitations, and computational complexity. To address this, we
propose a decentralized multi-agent decision-making framework for the search
and action problem with time constraints. The main idea is to treat time as an
allocated budget in a setting where each agent action incurs a time cost and
yields a certain reward. Our approach leverages probabilistic reasoning to make
near-optimal decisions leading to maximized reward. We evaluate our method in
the search, pick, and place scenario of the Mohamed Bin Zayed International
Robotics Challenge (MBZIRC), by using a probability density map and reward
prediction function to assess actions. Extensive simulations show that our
algorithm outperforms benchmark strategies, and we demonstrate system
integration in a Gazebo-based environment, validating the framework's readiness
for field application.
