The planning graph of Blum and Furst is one of the frequently used tools in planning. It is a data structure which can be visualized as a bipartite graph with state variables and actions as nodes and which approximates (upper bound) the set of reachable states with a given number of sets of simultaneous actions. We show that the contents of planning graphs follow from two more general notions: extended clause learning restricted to 2-literal clauses and the representation of parallel plans consisting of STRIPS actions in the classical propositional logic. This is the first time planning graphs have been given an explanation in terms of the inference methods used in SAT solvers. The work helps in bridging the gap between specialized algorithms devised for planning and general-purpose algorithms for automated reasoning.