  Game theory is playing more and more important roles in understanding complex
systems and in investigating intelligent machines with various uncertainties.
As a starting point, we consider the classical two-player zero-sum
linear-quadratic stochastic differential games, but in contrast to most of the
existing studies, the coefficient matrices of the systems are assumed to be
unknown to both players, and consequently it is necessary to study adaptive
strategies of the players, which may be termed as adaptive games and which has
rarely been explored in the literature. In this paper, it will be shown that
the adaptive strategies of both players can be constructed by the combined use
of a weighted least squares (WLS) estimation algorithm, a random regularization
method and a diminishing excitation method. Under almost the same structural
conditions as those in the traditional known parameters case, we will show that
the closed-loop adaptive game systems will be globally stable and
asymptotically reaches the Nash equilibrium as the time tends to infinity.
