In recent years some researchers have explored the use of reinforcement learning (RL)
algorithms as key components in the solution of various natural language processing (NLP) …

In the increasingly popular and diverse research area of social robotics, the primary goal is
to develop robot agents that exhibit socially intelligent behaviour while interacting in a face …

Trainable chatbots that exhibit fluent and human-like conversations remain a big challenge
in artificial intelligence. Deep Reinforcement Learning (DRL) is promising for addressing this …

Computational systems and objects are becoming increasingly closely integrated with our
daily activities. Ubiquitous and pervasive computing first identified the emerging challenges …

Interactive reinforcement learning methods utilise an external information source to evaluate
decisions and accelerate learning. Previous work has shown that human advice could …

Machine-learning based dialogue managers are able to learn complex behaviors in order to
complete a task, but it is not straightforward to extend their capabilities to new domains. We …

We describe our Alexa prize system (called 'Alana') which consists of an ensemble of bots,
combining rule-based and machine learning systems, and using a contextual ranking …

Even without speech recognition errors, robots may face difficulties interpreting natural-
language instructions. We present a method for robustly handling miscommunication …

Most of today's task-based spoken dialogue systems perform poorly if the user goal is not
within the system's task domain. On the other hand, chatbots cannot perform tasks involving …

Work on spoken dialogue systems (SDS) has largely been dominated by “slot filling”
applications for the past decade or more, where information-gathering tasks such as …