Humans understand language by extracting information (meaning) from sentences,
combining it with existing commonsense knowledge, and then performing reasoning to draw …

Understanding the meaning of a text is a fundamental challenge of natural language
understanding (NLU) research. An ideal NLU system should process a language in a way …

Driving an automobile involves the tasks of observing surroundings, then making a driving
decision based on these observations (steer, brake, coast, etc.). In autonomous driving, all …

Automated legal reasoning and its application in smart contracts and automated decisions
are increasingly attracting interest. In this context, ethical and legal concerns make it …

We present the AutoConcierge system that can “understand” human dialogs in a specific
domain, namely, restaurant recommendation. AutoConcierge will interactively “understand” …

FOLD-RM is an automated inductive learning algorithm for learning default rules for mixed
(numerical and categorical) data. It generates an (explainable) answer set programming …

FOLD-R is an automated inductive learning algorithm for learning default rules for mixed
(numerical and categorical) data. It generates an (explainable) normal logic program (NLP) …

" Rules as Code" in this paper is used to refer to a proposed methodology of legislative and
regulatory drafting. 1 That legislation can be represented in declarative code for automation …

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

In this paper, we present a system, called xASP, for generating explanations that explain
why an atom belongs to (or does not belong to) an answer set of a given program. The …