  The dependency graph is a data architecture that models all the dependencies
between the different types of assets in the game. It depicts the
dependency-based relationships between the assets of a game. For example, a
player must construct an arsenal before he can build weapons. It is vital that
the dependency graph of a game is designed logically to ensure a logical
sequence of game play. However, a mere logical dependency graph is not
sufficient in sustaining the players' enduring interests in a game, which
brings the problem of game balancing into picture. The issue of game balancing
arises when the players do not feel the chances of winning the game over their
AI opponents who are more skillful in the game play. At the current state of
research, the architecture of dependency graph is monolithic for the players.
The sequence of asset possession is always foreseeable because there is only a
single dependency graph. Game balancing is impossible when the assets of AI
players are overwhelmingly outnumbering that of human players. This paper
proposes a parallel architecture of dependency graph for the AI players and
human players. Instead of having a single dependency graph, a parallel
architecture is proposed where the dependency graph of AI player is adjustable
with that of human player using a support dependency as a game balancing
mechanism. This paper exhibits that the parallel dependency graph helps to
improve game balancing.

Artificial Intelligence