6 papers
Goal-Oriented Logic-based Semantic Communication for Neuro-Symbolic Reasoning with Applications onto Autonomous Driving
Ahmet Faruk Saz, Duo Xu, Faramarz Fekri
We consider First-Order Logic (FOL)-based semantic communication for neuro-symbolic decision-making in collaborative environments such as autonomous driving networks. Each connecte…
NeSyFS: A Neuro-symbolic Fast-Slow Thinking Framework for LLM Agent under Partial Observability
Duo Xu, Faramarz Fekri
Recently Large Language Models (LLMs) have been increasingly deployed as autonomous agents in applications such as self-reflection, retrieval-augmented generation, and scientific d…
Reinforcement Learning-Augmented LLM Agents for Collaborative Decision Making and Performance Optimization
Dong Qiu, Duo Xu, Limengxi Yue
Large Language Models (LLMs) perform well in language tasks but often lack collaborative awareness and struggle to optimize global performance in multi-agent settings. We present a…
Learning Hidden Subgoals under Temporal Ordering Constraints in Reinforcement Learning
Duo Xu, Faramarz Fekri
In real-world applications, the success of completing a task is often determined by multiple key steps which are distant in time steps and have to be achieved in a fixed time order…
Generalization of Compositional Tasks with Logical Specification via Implicit Planning
Duo Xu, Faramarz Fekri
In this study, we address the challenge of learning generalizable policies for compositional tasks defined by logical specifications. These tasks consist of multiple temporally ext…
LLM-Augmented Symbolic Reinforcement Learning with Landmark-Based Task Decomposition
Alireza Kheirandish, Duo Xu, Faramarz Fekri
One of the fundamental challenges in reinforcement learning (RL) is to take a complex task and be able to decompose it to subtasks that are simpler for the RL agent to learn. In th…