About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
IJCAI 2021
Conference paper
Efficient Black-box Planning using Macro Actions with Focused Effects
Abstract
The difficulty of classical planning increases exponentially with search-tree depth. Heuristic search can make planning more efficient, but good heuristics can be expensive to compute or may require domain-specific information, and such information may not even be available in the more general case of black-box planning. Rather than treating a given planning problem as fixed and carefully constructing a heuristic to match it, we instead rely on the simple and general-purpose “goal-count†heuristic and construct macro actions to make it more accurate. Our approach searches for macro-actions with focused effects (i.e. macros that modify only a small number of state variables), which align well with the assumptions made by the goal-count heuristic. Our method discovers macros that dramatically improve black-box planning efficiency across a wide range of planning domains, including Rubik’s cube, where it generates fewer states than the state-of-the-art LAMA planner with access to the full SAS+ representation.