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cs.AI2026
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…
cs.AI2026
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…