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TIC: Translate-Infer-Compile for accurate 'text to plan' using LLMs and logical intermediate representations
We leverage the strengths of both the techniques by using an LLM for generating the PDDL representation (task PDDL) of planning task requests followed by using a classical planner for computing a plan.
A Scalable Technique for Weak-Supervised Learning with Domain Constraints
We propose a novel scalable end-to-end pipeline that uses symbolic domain knowledge as constraints for learning a neural network for classifying unlabeled data in a weak-supervised manner.
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