"""One-ply strategy example. All responses reuse an action supplied by the server."""
import json
import sys

BEATS = {"ROCK": "SCISSORS", "SCISSORS": "PAPER", "PAPER": "ROCK"}


def cell(position):
    return position["row"], position["col"]


def distance(a, b):
    return max(abs(a[0] - b[0]), abs(a[1] - b[1]))


def evaluate(action, request):
    if action["type"] != "MOVE":
        return 0
    pieces = [p for p in request["state"]["board"]["pieces"] if p["status"] == "ALIVE"]
    by_id = {p["id"]: p for p in pieces}
    board = {cell(p["position"]): p for p in pieces}
    attacker = by_id[action["pieceId"]]
    origin, target = cell(action["from"]), cell(action["to"])
    defender = board.get(target)
    score = 0
    del board[origin]

    if defender:
        if attacker["type"] == defender["type"]:
            return -25  # Same type: neither moves or dies; avoid wasting a turn.
        if BEATS[attacker["type"]] != defender["type"]:
            return -100  # Attacker disappears; defender remains.
        score += 100
    board[target] = attacker  # Moving onto an empty cell or winning combat.
    state_board = request["state"]["board"]
    goal = (0, state_board["cols"] - 1) if request["player"] == "P1" else (state_board["rows"] - 1, 0)
    home = (state_board["rows"] - 1, 0) if request["player"] == "P1" else (0, state_board["cols"] - 1)
    if target == goal:
        return 1_000_000
    score += 4 * (distance(origin, goal) - distance(target, goal))

    enemies = [(pos, p) for pos, p in board.items() if p["ownerId"] != request["player"]]
    guard = board.get(home)
    if any(distance(pos, home) == 1 and (guard is None or BEATS[p["type"]] == guard["type"])
           for pos, p in enemies):
        score -= 1_000_000
    threats = sum(
        distance(pos, target) == 1
        and BEATS[p["type"]] == attacker["type"]
        for pos, p in enemies
    )
    score -= 120 * threats
    prey = [pos for pos, p in enemies if p["type"] == BEATS[attacker["type"]]]
    if prey:
        score += 2 * (min(distance(origin, pos) for pos in prey) - min(distance(target, pos) for pos in prey))
    return score


def choose_action(request):
    return max(request["legalActions"], key=lambda action: evaluate(action, request))


def main():
    for line in sys.stdin:
        request = json.loads(line)
        print(json.dumps({"type": "ACTION", "sequence": request["sequence"],
                          "action": choose_action(request)}, separators=(",", ":")), flush=True)


if __name__ == "__main__":
    main()