5 papers
Adversarial Inverse Reinforcement Learning for Mean Field Games
Yang Chen, Libo Zhang, Jiamou Liu +1
Mean field games (MFGs) provide a mathematically tractable framework for modelling large-scale multi-agent systems by leveraging mean field theory to simplify interactions among ag…
A Community-driven vision for a new Knowledge Resource for AI
Vinay K Chaudhri, Chaitan Baru, Brandon Bennett +29
The long-standing goal of creating a comprehensive, multi-purpose knowledge resource, reminiscent of the 1984 Cyc project, still persists in AI. Despite the success of knowledge re…
Meta-Inverse Reinforcement Learning for Mean Field Games via Probabilistic Context Variables
Yang Chen, Xiao Lin, Bo Yan +4
Designing suitable reward functions for numerous interacting intelligent agents is challenging in real-world applications. Inverse reinforcement learning (IRL) in mean field games…
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning
Qiming Bao, Alex Yuxuan Peng, Zhenyun Deng +10
Combining large language models with logical reasoning enhances their capacity to address problems in a robust and reliable manner. Nevertheless, the intricate nature of logical re…
Assessing and Enhancing the Robustness of Large Language Models with Task Structure Variations for Logical Reasoning
Qiming Bao, Gael Gendron, Alex Yuxuan Peng +5
Large language models (LLMs), such as LLaMA, Alpaca, Vicuna, GPT-3.5 and GPT-4, have advanced the performance of AI systems on various natural language processing tasks to human-li…