Our work on Macro Operator Synthesis for ADL Domains has been accepted at the European Conference on Artificial Intelligence (ECAI) 2020. It investigates how a sequence of ADL operators can be combined into a single macro operator, which can be used e.g., for improving the planner performance or for maintaining a plan library.
A macro operator is a planning operator that is generated from a sequence of actions. Macros have mostly been considered for macro planning, where the planner considers the macro as a single action and expands it into the original sequence during execution, but they can also be applied to other problems, such as maintaining a plan libray. There are several approaches to macro operator generation, which differ in restrictions on the original actions and in the way they represent macros. However, all existing approaches are either restricted to STRIPS domains or they do not synthesize macros but consider the original sequence instead. We study the synthesis of macro operators for ADL domains. We describe how to compute the parameterized precondition and effects of a macro operator such that they are equivalent to the precondition and effects of the respective action sequence and prove the correctness of the synthesized macro operators based on a Situation Calculus semantics for ADL. We use the synthesis method for ADL macro planning and evaluate it on a number of domains from the IPC. We also describe how macro operator synthesis can be useful for maintaining a plan library by computing the precondition and effects of the parameterized library plans. Finally, we sketch how macro operator synthesis can also be used in the context of continual planning and interruptible task execution.