Abstract: Some embodiments of the present invention are directed to a method of choosing the components for a satisficing planner using machine learning (ML) (for example, deep learning (DL)). Some embodiments of the present invention are directed to choosing search algorithm component(s) for a satisficing planner using ML (for example, DL). Some embodiments of the present invention are directed to choosing search refinement components (that is, search boosting component(s) and/or search pruning component(s)) for a satisficing planner using ML (for example, DL).

Abstract: Embodiments for improving heuristic searching in artificial intelligence (AI) planning are provided. At least one planning task within a planning domain is identified. The at least one planning task is solved to generate a plurality of plans. The plurality of plans include a plurality of state-action pairs. A neural network is trained utilizing the plurality of state-action pairs. Preferred actions for states within the planning domain are determined utilizing the neural network.