A Planning Compilation to Reason about Goal Achievement at Planning Time
This addresses a specific challenge in automated planning for AI applications, but it is incremental as it builds on existing planning methods.
The paper tackles the problem of identifying permanent goal-achieving actions in planning tasks, traditionally done post-search, by proposing a compilation that adds commit actions to enforce goal persistence, enabling identification during planning without additional overhead in optimal and suboptimal planning.
Identifying the specific actions that achieve goals when solving a planning task might be beneficial for various planning applications. Traditionally, this identification occurs post-search, as some actions may temporarily achieve goals that are later undone and re-achieved by other actions. In this paper, we propose a compilation that extends the original planning task with commit actions that enforce the persistence of specific goals once achieved, allowing planners to identify permanent goal achievement during planning. Experimental results indicate that solving the reformulated tasks does not incur on any additional overhead both when performing optimal and suboptimal planning, while providing useful information for some downstream tasks.