If you use Monte Carlo Tree Search (MCTS) to implement an AI…
Questions
If yоu use Mоnte Cаrlо Tree Seаrch (MCTS) to implement аn AI agent to play a two-player, zero-sum game, you will need a playout (rollout) policy. The playout policy determines how the simulation proceeds from a newly expanded node until a terminal state is reached. It is very important that this policy is not random, since it does not model a realistic opponent and makes MCTS struggle to identify critical game-ending scenarios. For chess, one example of a non-random playout policy consists of picking moves according to the following criteria: (1) move to capture a piece, (2) move to avoid immediate capture [if (1) is not available], or (3) move randomly [if (2) is not available]. Notice that a non-random playout policy can still involve performing random moves. Battleship is a classic 2-player naval strategy game. The goal is to secretly place a fleet of 5 ships on your 10x10 grid (2 ships taking 3 squares and the remainin ships taking 2, 4, and 5 squares), take turns calling out coordinates on your opponent's grid, and sink all their ships before they sink yours. You sink a ship after calling out all coordinates occupied by that ship. You will create an AI agent to play the Battleship game using MCTS. Please outline a non-random playout policy for this agent.