  Board games, with the exception of solo games, need at least one other player
to play. Because of this, we created Artificial Intelligent (AI) agents to play
against us when an opponent is missing. These AI agents are created in a number
of ways, but one challenge with these agents is that an agent can have superior
ability compared to us. In this work, we describe how to create weaker AI
agents that play board games. We use Tic-Tac-Toe, Nine-Men's Morris, and
Mancala, and our technique uses a Reinforcement Learning model where an agent
uses the Q-learning algorithm to learn these games. We show how these agents
can learn to play the board game perfectly, and we then describe our approach
to making weaker versions of these agents. Finally, we provide a methodology to
compare AI agents.
