7 papers
A Neurosymbolic Approach for Constructing Planning Domain Models from Clinical Narratives
Ranveer Singh, Saurabh Mathur, Michael Skinner +3
Surgical procedures such as laparoscopic appendectomy are complex, high-stakes processes, yet formalizing their workflows for decision support remains a significant challenge. Indu…
Training the Orchestrator: A Supervised Approach to End-to-End PDDL Planning with LLM Agents
Rajesh Mangannavar, Zachary Coalson, Pranay Dugar +1
Translating natural-language planning intent into verified plans is a longstanding challenge: people communicate goals in language, while classical planners require formal PDDL spe…
GammaZero: Learning To Guide POMDP Belief Space Search With Graph Representations
Rajesh Mangannavar, Prasad Tadepalli
We introduce an uncertainty-aware graph representation framework for learning to guide planning in Partially Observable Markov Decision Processes (POMDPs). Unlike existing approach…
Graph Neural Network Based Action Ranking for Planning
Rajesh Mangannavar, Stefan Lee, Alan Fern +1
We propose a novel approach to learn relational policies for classical planning based on learning to rank actions. We introduce a new graph representation that explicitly captures…
Hierarchical Object-Oriented POMDP Planning for Object Rearrangement
Rajesh Mangannavar, Alan Fern, Prasad Tadepalli
We present an online planning framework and a new benchmark dataset for solving multi-object rearrangement problems in partially observable, multi-room environments. Current object…
Self-attention-based Diffusion Model for Time-series Imputation in Partial Blackout Scenarios
Mohammad Rafid Ul Islam, Prasad Tadepalli, Alan Fern
Missing values in multivariate time series data can harm machine learning performance and introduce bias. These gaps arise from sensor malfunctions, blackouts, and human error and…