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.

Agent based simulation of social organizations, via the investigation of agents' training and learning tactics and strategies, has been inspired by the ability of humans to learn from social environments which are rich in agents, interactions and partial or hidden information. Such richness is a source of complexity that an effective learner has to be able to navigate. This paper focuses on the investigation of the impact of the environmental complexity on the game playing-and-learning behavior of synthetic agents. We demonstrate our approach using two independent turn-based zero-sum games as the basis of forming social events which are characterized both by competition and cooperation. The paper's key highlight is that as the complexity of a social environment changes, an effective player has to adapt its learning and playing profile to maintain a given performance profile

Long-time world checkers champion Marion Tinsley consistently bested all comers, losing only nine games in the 40 years following his 1954 crowning. He lost his world championship title to a computer program in 1994 and now that same program has become unbeatable; its creators have proved that even a perfectly played game against it will end in a draw. Jonathan Schaeffer and his team at the University of Alberta, Canada, have been working on their program, called Chinook, since 1989, running calculations on as many as 200 computers simultaneously. Schaeffer has now announced that they have solved the game of American checkers, which is played on an 8 by 8 board and is also known as English draughts. The team directed Chinook so it didn't have to go through every one of the 500 billion billion (5 * 1020) possible moves.