General Game Playing (GGP) is an area of research in artificial intelligence that focuses on the representation of games in terms of an abstract language known as Game Description Language (GDL). The abstract nature of that language allows for the development of intelligent agents that without modification can perform competently on games that they have never seen before based on known properties of GDL’s uniform and compact syntax. Although GDL is an effective tool for communication, it is less useful as a representational framework for reasoning about games. In its place, the Stanford Logic Group has developed a new class of behavioral models, called Propositional Nets (PNs), with which an algorithm has been designed for determining whether games can be decomposed into independent sub-systems that can be reasoned about independently of one another.