Distributed cooperative robots can be highly beneficial in mapping disaster environments and assisting with search and rescue operations. In most situations such environments only allow for only limited communication between robots. This thesis reports on simulation experiments conducted to test the impact of having only partial communication capabilities between cooperative agents on area exploration strategies. The Monte Carlo Tree Search (MCTS) planning algorithm has been utilised by multi-robot teams to cooperate and explore an environment effectively. On top of this base case, other communication scenarios are applied: No communication at all, and near-neighbor communication at various ranges. In addition to these communication strategies, robots are also given the ability to predict the paths of peers. From extensive simulation tests, it is shown that partial communication can recover a significant amount of performance in a limited communication environment. Giving agents a peer prediction ability is shown to have a positive effect in very specific situations. It is also shown that providing prior information of the environment obstacle locations to agents is not useful. Instead, increasing the number of chances of agents sharing information, positively effects the exploration performance.