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Found 6 papers, 2 papers with code
Planning with Language Models Through The Lens of Efficiency
We analyse the cost of using LLMs for planning and highlight that recent trends are profoundly uneconomical.
Some Orders Are Important: Partially Preserving Orders in Top-Quality Planning
The ability to generate multiple plans is central to using planning in real-life applications.
Unifying and Certifying Top-Quality Planning
We show that task transformations found in the existing literature can be employed for the efficient certification of various top-quality planning problems and propose a novel transformation to efficiently certify loopless top-quality planning.
Hierarchical Reinforcement Learning with AI Planning Models
Our approach defines options in hierarchical reinforcement learning (HRL) from AIP operators by establishing a correspondence between the state transition model of AI planning problem and the abstract state transition system of a Markov Decision Process (MDP).
Reinforcement Learning for Classical Planning: Viewing Heuristics as Dense Reward Generators
In this paper, we propose to leverage domain-independent heuristic functions commonly used in the classical planning literature to improve the sample efficiency of RL.
Knowledge Engineering for Planning-Based Hypothesis Generation
To capture the model description we propose a language called LTS++ and a web-based tool that enables the specification of the LTS++ model and a set of observations.
