  Cooperative task assignment is an important subject in multi-agent systems
with a wide range of applications. These systems are usually designed with
massive communication among the agents to minimize the error in pursuit of the
general goal of the entire system. In this work, we propose a novel approach
for Decentralized Cooperative Communication-less Multi-Agent Task Assignment
(DCCMATA) employing Monte-Carlo Tree Search (MCTS). Here, each agent can assign
the optimal task by itself for itself. We design the system to automatically
maximize the success rate, achieving the collective goal effectively. To put it
another way, the agents optimally compute each following step, only by knowing
the current location of other agents, with no additional communication
overhead. In contrast with the previously proposed methods which rely on the
task assignment procedure for similar problems, we describe a method in which
the agents move towards the collective goal. This may lead to scenarios where
some agents not necessarily move towards the closest goal. However, the total
efficiency (makespan) and effectiveness (success ratio) in these cases are
significantly improved. To evaluate our approach, we have tested the algorithm
with a wide range of parameters(agents, size, goal). Our implementation
completely solves (Success Rate = %100) a 20*20 grid with 20 goals by 20 agents
in 7.9 s runtime for each agent. Also, the proposed algorithm runs with the
complexity of O(N^2I^2 + IN^4), where the I and N are the MCTS iterative index
and grid size, respectively.
