1 citations · 1 across the 5 of their papers we have counts for
5 papers
CEDAR: Automata as Verifiable Interfaces for Language-Guided Embodied Action
Lekai Chen, Alvaro Velasquez, Ashutosh Trivedi
Natural-language tasking of embodied agents is rarely just goal specification: users also impose constraints that must persist while the world changes. Code-generating LLM agents c…
Explaining Hitori Puzzles: Neurosymbolic Proof Staging for Sequential Decisions
Maria Leonor Pacheco, Fabio Somenzi, Dananjay Srinivas +1
We propose a neurosymbolic approach to the explanation of complex sequences of decisions that combines the strengths of decision procedures and Large Language Models (LLMs). We dem…
Explaining Puzzle Solutions in Natural Language: An Exploratory Study on 6x6 Sudoku
Anirudh Maiya, Razan Alghamdi, Maria Leonor Pacheco +2
The success of Large Language Models (LLMs) in human-AI collaborative decision-making hinges on their ability to provide trustworthy, gradual, and tailored explanations. Solving co…
Average Reward Reinforcement Learning for Omega-Regular and Mean-Payoff Objectives
Milad Kazemi, Mateo Perez, Fabio Somenzi +3
Recent advances in reinforcement learning (RL) have renewed interest in reward design for shaping agent behavior, but manually crafting reward functions is tedious and error-prone.…
LLMs as Probabilistic Minimally Adequate Teachers for DFA Learning
Lekai Chen, Ashutosh Trivedi, Alvaro Velasquez
The emergence of intelligence in large language models (LLMs) has inspired investigations into their integration into automata learning. This paper introduces the probabilistic Min…