The main focus of the dissertation is on the method of integration of causal and resource reasoning that can expedite problem solving by graphbased planners.  We present a representation of the lifted planned graph constructed by the MADPlan planner, which is populated with exclusivity relations and binary constraints.  Moreover, the lifted nodes that appear within the graph maintain references to classes of resources which constitute the domain of the uninstantiated variables appearing in the lifted nodes.  The exclusivity relations, binary constraints, and resource references are used to guide the search towards the extraction of executable plans. We discuss the search method used in MADPlan that achieves the extraction of grounded plans directly from lifted planning graphs.  The main characteristic of the search is that it can switch between two different backtracking methods.  During chronological backtracking the algorithm tries a new resource allocation for a given node only when the node appears in the conflict set.  Otherwise the algorithm immediately backtracks further (backjumps) ignoring the remaining resource allocations for the last visited node.  The contributions discussed above allow our planner to scale very well with the increasing number of resources that appear in planning. The above contributions are direct extensions to the graphbased planning paradigm and they can be utilised by all graphbased planners.