Briefing: When both Grounding and not Grounding are Bad -- A Partially Grounded Encoding of Planning into SAT (Extended Version)
Strategic angle: Exploring the complexities of classical planning problems and their representations.
Classical planning problems are often framed using lifted first-order representations, which provide a balance of compactness and generality. However, the common practice of grounding these representations can lead to significant drawbacks.
The newly proposed partially grounded encoding seeks to mitigate these issues by maintaining the advantages of lifted representations while addressing the inefficiencies introduced by full grounding.
This extended version of the study, published on March 23, 2026, offers insights into the complexities of planning and aims to enhance the architectural framework for future implementations in AI planning systems.