Charu C. Aggarwal

“Monte Carlo trees tend to explore a few promising branches deeper based on evaluations from previous experience, whereas minimax trees explore all unpruned branches in a roughly similar way. The human approach to chess is similar to the former, wherein humans evaluate a small number of promising directions of play rather than exhaustively considering all possibilities. The result is that the style of chess from Monte Carlo tree search is more similar to humans than that from minimax trees. The programs resulting from Monte Carlo trees can often take more risks in game playing, if past experience has shown that such risks are warranted over the longer term. On the other hand, minimax trees tend to discourage any risks beyond the horizon of tree exploration, especially since the evaluations at leaf levels are imperfect.”
― Charu C. Aggarwal, Artificial Intelligence: A Textbook

“A key point is that an increased number of attributes relative to training points provides additional degrees of freedom to the optimization problem, as a result of which irrelevant solutions become more likely. Therefore, a natural solution is to add a penalty for using additional features. Specifically we can add a penalty for each parameter w i, which is non-zero. One can express this penalty using the L 0-norm of the vector”
― Charu C. Aggarwal, Artificial Intelligence: A Textbook