This tutorial surveys neural approaches to conversational AI that were developed in the last
few years. We group conversational systems into three categories:(1) question answering …

We give an overview of recent exciting achievements of deep reinforcement learning (RL).
We discuss six core elements, six important mechanisms, and twelve applications. We start …

Artificial agents have traditionally been trained to maximize reward, which may incentivize
power-seeking and deception, analogous to how next-token prediction in language models …

Abstract As Large Language Models (LLMs) have shown significant intelligence, the
progress to leverage LLMs as planning modules of autonomous agents has attracted more …

Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …

Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …

We introduce SwiftSage, a novel agent framework inspired by the dual-process theory of
human cognition, designed to excel in action planning for complex interactive reasoning …

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …

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) …

There has been growing interest in using neural networks and deep learning techniques to
create dialogue systems. Conversational recommendation is an interesting setting for the …