Parallel Lifted Planning via Semi-Naive Datalog Evaluation

2026-05-08

AI Summary

This work addresses the computational inefficiency of grounding and poor search performance in classical planning by proposing an efficient semi-naive Datalog evaluation framework that supports lifted planning. The approach introduces, for the first time, a two-level parallel execution model operating at both the rule and grounding levels, and extends a clique-enumeration-based planner-specific grounder to support semi-naive evaluation. Integrated with the FF heuristic and greedy best-first search, the system outperforms existing baselines even on a single core. On challenging grounding-intensive tasks, the Datalog engine achieves a parallelism degree of 92.4%, yielding up to a sixfold speedup on an 8-core configuration.

Abstract

Lifted classical planners operate directly on first-order planning tasks to avoid the computationally demanding grounding step. However, lifted planning is typically slower, as planners must repeatedly instantiate ground structures during search. Many core components of lifted classical planning, such as successor generation, axiom evaluation, task grounding, and delete-relaxed heuristics, have previously been studied through the lens of Datalog evaluation. We build upon this line of work and extend it by developing and analyzing an execution model with two levels of parallelism: rule-level parallelism and grounding parallelism. We further specialize this solver for planning-specific workloads with a grounder based on clique enumeration, which we extend to support semi-naive Datalog evaluation. Our experimental evaluation using greedy best-first search with the FF heuristic shows that our implementation already solves more tasks than the baselines on a single core, and the gap widens as additional cores are used. Moreover, on hard-to-ground tasks where on average 97.6% of the total runtime is spent in Datalog execution, the proposed execution model exhibits an average parallel fraction of 92.4%, while achieving up to a 6-fold speedup on 8 cores in practice.

Problem

• lifted planning
• Datalog evaluation
• parallelism
• grounding
• classical planning

Innovation

• lifted planning
• semi-naive Datalog evaluation
• parallelism
• clique enumeration
• first-order planning