... The relations between P (q, t, z) and these probabilities p win , p washout , p tenpai , and p lose are P (q, t, win) = p win (q, t) P (q, t, tenpai) = p win (q, t)p washout (q, t)p tenpai (q, t) P (q, t, noten) = p win (q, t)p washout (q, t)p tenpai (q, t) P (q, t, lose) = p win (q, t)p washout (q, t)p lose (q, t) P (q, t, other) = p win (q, t)p washout (q, t)p lose (q, t) (18) These probabilities are inferred by logistic regression using features that are the results of value evaluations of these MDPs. To explain their features, let us introduce the following symbols: V win (q, t) and P win (q, t) are values from M win , where the former is a state value of (q, null, S Fold , t) and the latter is the probability that i in this state finally chooses an action in A Wins ; P tenpai (q, t) is the probability that i in (q, null, S Fold , t) of M tenpai will have a tenpai hand when it terminates; and P Lose (q, t) and U LoseAverage (q, t) are values from Eqs. (14) and (15), where the initial hand of M fold is q and T is adjusted according to t. The features used for the regressions are as follows. ...