Abstract Advocates for Neuro-Symbolic Artificial Intelligence (NeSy) assert that combining
deep learning with symbolic reasoning will lead to stronger AI than either paradigm on its …

In this work, we explore techniques for augmenting interactive agents with information from
symbolic modules, much like humans use tools like calculators and GPS systems to assist …

Text-based reinforcement learning agents have predominantly been neural network-based
models with embeddings-based representation, learning uninterpretable policies that often …

Text-based games (TBGs) have emerged as an important collection of NLP tasks, requiring
reinforcement learning (RL) agents to combine natural language understanding with …

Background: The field of Artificial Intelligence has undergone cyclical periods of growth and
decline, known as AI summers and winters. Currently, we are in the third AI summer …

Text-based games offer a challenging test bed to evaluate virtual agents at language
understanding, multi-step problem-solving, and common-sense reasoning. However, speed …

Model-based reinforcement learning has shown great results when using deep neural
networks for learning world models. However, these results are not directly applicable to …

Explainability is an essential reason limiting the application of neural networks in many vital
fields. Although neuro-symbolic AI hopes to enhance the overall explainability by leveraging …

Neuro-symbolic AI is an effective method for improving the overall performance of AI models
by combining the advantages of neural networks and symbolic learning. However, there are …

Advocates for Neuro-Symbolic Artificial Intelligence (NeSy) assert that combining deep
learning with symbolic reasoning will lead to stronger AI than either paradigm on its own. As …