A wide range of real-world applications is characterized by their symbolic nature,
necessitating a strong capability for symbolic reasoning. This paper investigates the …

The goal of the growing discipline of neuro-symbolic artificial intelligence (AI) is to develop
AI systems with more human-like reasoning capabilities by combining symbolic reasoning …

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 (TBG) have emerged as promising environments for driving research in
grounded language understanding and studying problems like generalization and sample …

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

Automating commonsense reasoning, ie, automating the human thought process, has been
considered fiendishly difficult. It is widely believed that automation of commonsense …

Automating commonsense reasoning, ie, automating the human thought process, has been
considered fiendishly difficult. It is widely believed that automation of commonsense …

The ability to reason, plan and solve highly abstract problems is a hallmark of human
intelligence. Recent advancements in artificial intelligence, propelled by deep neural …

Two common approaches to sequential decision-making are AI planning (AIP) and
reinforcement learning (RL). Each has strengths and weaknesses. AIP is interpretable, easy …