Abstract We address the problem of making general video game playing agents play in a human-like manner. To this end, we introduce several modififications of the UCT formula used in Monte Carlo Tree Search that biases action selection towards repeating the current action, making pauses, and limiting rapid switching between actions. Playtraces of human players are used to model their propensity for repeated actions; this model is used for biasing the UCT formula. Experiments show that our modifified MCTS agent, called BoT, plays quantitatively similar to human players as measured by the distribution of repeated actions. A survey of human observers reveals that the agent exhibits human-like playing style in some games but not others