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LLM+Reasoning+Planning for supporting incomplete user queries in presence of APIs
The key idea behind our proposed approach is to leverage logical reasoning and classical AI planning along with an LLM for accurately answering user queries including identification and gathering of any missing information in these queries.
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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