Commonsense reasoning is in principle a central problem in artificial intelligence, but it is a
very difficult one. One approach that has been pursued since the earliest days of the field …

We consider problems of sequential robot manipulation (aka. combined task and motion
planning) where the objective is primarily given in terms of a cost function over the final …

Deep reinforcement learning (DRL) has gained great success by learning directly from high-
dimensional sensory inputs, yet is notorious for the lack of interpretability. Interpretability of …

To endow computers with common sense is one of the major long-term goals of artificial
intelligence research. One approach to this problem is to formalize commonsense reasoning …

Reinforcement learning and symbolic planning have both been used to build intelligent
autonomous agents. Reinforcement learning relies on learning from interactions with real …

Intelligent robots frequently need to understand requests from naive users through natural
language. Previous approaches either cannot account for language variation, eg, keyword …

Recent progress in both AI and robotics have enabled the development of general purpose
robot platforms that are capable of executing a wide variety of complex, temporally extended …

Neural-symbolic computing (NeSy), which pursues the integration of the symbolic and
statistical paradigms of cognition, has been an active research area of Artificial Intelligence …

This article describes REBA, a knowledge representation and reasoning architecture for
robots that is based on tightly-coupled transition diagrams of the domain at two different …

In this survey, we present an overview on (Modal) Temporal Logic Programming in view of
its application to Knowledge Representation and Declarative Problem Solving. The syntax of …