Search Results for author:
Found 14 papers, 7 papers with code
Implicit Abstraction Heuristics
Indeed, some of the power of the explicit abstraction heuristics comes from precomputing the heuristic function offline and then determining h(s) for each evaluated state s by a very fast lookup in a database.
Online Planner Selection with Graph Neural Networks and Adaptive Scheduling
Automated planning is one of the foundational areas of AI.
IPC: A Benchmark Data Set for Learning with Graph-Structured Data
Benchmark data sets are an indispensable ingredient of the evaluation of graph-based machine learning methods.
Ranked #2 on
Graph Classification
on IPC-lifted
Efficient Black-Box Planning Using Macro-Actions with Focused Effects
Focused macros dramatically improve black-box planning efficiency across a wide range of planning domains, sometimes beating even state-of-the-art planners with access to a full domain model.
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.
Efficient Meta Subspace Optimization
Subspace optimization methods have the attractive property of reducing large-scale optimization problems to a sequence of low-dimensional subspace optimization problems.
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).
Reinforced Meta Active Learning
In stream-based active learning, the learning procedure typically has access to a stream of unlabeled data instances and must decide for each instance whether to label it and use it for training or to discard it.
Generalized Planning in PDDL Domains with Pretrained Large Language Models
We investigate whether LLMs can serve as generalized planners: given a domain and training tasks, generate a program that efficiently produces plans for other tasks in the domain.
Can LLMs Fix Issues with Reasoning Models? Towards More Likely Models for AI Planning
This is the first work to look at the application of large language models (LLMs) for the purpose of model space edits in automated planning tasks.
Choosing a Classical Planner with Graph Neural Networks
Thus, the ability to predict their performance on a given problem is of great importance.
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.
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.
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.
