6 citations · 15 across the 12 of their papers we have counts for
12 papers
Learning Admissible Heuristics via Cost Partitioning
Hugo Barral, Quentin Cappart, Marie-José Huguet +1
Admissible heuristics are essential for optimal planning, yet learning them remains challenging due to the risk of overestimation. Cost partitioning combines multiple abstraction h…
Learning Lifted Action Models from Unsupervised Visual Traces
Kai Xi, Stephen Gould, Sylvie Thiébaux
Efficient construction of models capturing the preconditions and effects of actions is essential for applying AI planning in real-world domains. Extensive prior work has explored l…
On the Ability of Transformers to Verify Plans
Yash Sarrof, Yupei Du, Katharina Stein +3
Transformers have shown inconsistent success in AI planning tasks, and theoretical understanding of when generalization should be expected has been limited. We take important steps…
Exploring Plan Space through Conversation: An Agentic Framework for LLM-Mediated Explanations in Planning
Guilhem Fouilhé, Rebecca Eifler, Antonin Poché +2
When automating plan generation for a real-world sequential decision problem, the goal is often not to replace the human planner, but to facilitate an iterative reasoning and elici…
AI Planning: A Primer and Survey (Preliminary Report)
Dillon Z. Chen, Pulkit Verma, Siddharth Srivastava +2
Automated decision-making is a fundamental topic that spans multiple sub-disciplines in AI: reinforcement learning (RL), AI planning (AP), foundation models, and operations researc…
Graph Learning for Planning: The Story Thus Far and Open Challenges
Dillon Z. Chen, Mingyu Hao, Sylvie Thiébaux +1
Graph learning is naturally well suited for use in planning due to its ability to exploit relational structures exhibited in planning domains and to take as input planning instance…
Graph Learning for Numeric Planning
Dillon Z. Chen, Sylvie Thiébaux
Graph learning is naturally well suited for use in symbolic, object-centric planning due to its ability to exploit relational structures exhibited in planning domains and to take a…
Novelty Heuristics, Multi-Queue Search, and Portfolios for Numeric Planning
Dillon Z. Chen, Sylvie Thiébaux
Heuristic search is a powerful approach for solving planning problems and numeric planning is no exception. In this paper, we boost the performance of heuristic search for numeric…
Return to Tradition: Learning Reliable Heuristics with Classical Machine Learning
Dillon Z. Chen, Felipe Trevizan, Sylvie Thiébaux
Current approaches for learning for planning have yet to achieve competitive performance against classical planners in several domains, and have poor overall performance. In this w…
Learning Domain-Independent Heuristics for Grounded and Lifted Planning
Dillon Z. Chen, Sylvie Thiébaux, Felipe Trevizan
We present three novel graph representations of planning tasks suitable for learning domain-independent heuristics using Graph Neural Networks (GNNs) to guide search. In particular…
A More General Theory of Diagnosis from First Principles
Alban Grastien, Patrik Haslum, Sylvie Thiébaux
Model-based diagnosis has been an active research topic in different communities including artificial intelligence, formal methods, and control. This has led to a set of disparate…
Heuristic Search for Multi-Objective Probabilistic Planning
Dillon Chen, Felipe Trevizan, Sylvie Thiébaux
Heuristic search is a powerful approach that has successfully been applied to a broad class of planning problems, including classical planning, multi-objective planning, and probab…