In this book, we explore the task of automatically predicting human decision-making and its use in designing intelligent human-aware automated computer systems of varying natures - from purely conflicting interaction settings (e.g., security and games) to fully cooperative interaction settings (e.g., autonomous driving and personal robotic assistants). We explore the techniques, algorithms, and empirical methodologies for meeting the challenges that arise from the above tasks and illustrate major benefits from the use of these computational solutions in real-world application domains such as security, negotiations, argumentative interactions, voting systems, autonomous driving, and games. The book presents both the traditional and classical methods as well as the most recent and cutting edge advances, providing the reader with a panorama of the challenges and solutions in predicting human decision-making. Top Description Table of Contents Author Information Table of Contents Preface Acknowledgments Introduction Utility Maximization Paradigm Predicting Human Decision-Making From Human Prediction to Intelligent Agents Which Model Should I Use? Concluding Remarks Bibliography Authors' Biographies Index Top Description Table of Contents Author Information About the Author(s)Ariel Rosenfeld, Weizmann Institute of Science Ariel Rosenfeld is a Koshland Postdoctoral Fellow at Weizmann Institute of Science, Israel. He obtained a B.Sc. in Computer Science and Economics, graduating magna cum laude from Tel Aviv University, and a Ph.D. in Computer Science from Bar-Ilan University.

This article is part of CMO.com's December series about 2018 trends, predictions, and new opportunities. Despite a vibrant economy, individual companies will confront unceasing changes in technology and inflated consumer expectations. That's why Forrester Research is calling 2018 a "year of reckoning." It sees both as an existential threat that makes the fate of individual companies uncertain. This environment has prompted a radical shift in what is traditionally meant by marketing; some even view the traditional role of chief marketing officer as outmoded.

To facilitate the best end-to-end experience possible for users, Uber is committed to making customer support easier and more accessible. Working toward this goal, Uber's Customer Obsession team leverages five different customer-agent communication channels powered by an in-house platform that integrates customer support ticket context for easy issue resolution. With hundreds of thousands of tickets surfacing daily on the platform across 400 cities worldwide, this team must ensure that agents are empowered to resolve them as accurately and quickly as possible.

In repeated stochastic games (RSGs), an agent must quickly adapt to the behavior of previously unknown associates, who may themselves be learning. This machine-learning problem is particularly challenging due, in part, to the presence of multiple (even infinite) equilibria and inherently large strategy spaces. In this paper, we introduce a method to reduce the strategy space of two-player general-sum RSGs to a handful of expert strategies. This process, called Mega, effectually reduces an RSG to a bandit problem. We show that the resulting strategy space preserves several important properties of the original RSG, thus enabling a learner to produce robust strategies within a reasonably small number of interactions. To better establish strengths and weaknesses of this approach, we empirically evaluate the resulting learning system against other algorithms in three different RSGs.

A Behavior Tree (BT) is a way to structure the switching between different tasks in an autonomous agent, such as a robot or a virtual entity in a computer game. BTs are a very efficient way of creating complex systems that are both modular and reactive. These properties are crucial in many applications, which has led to the spread of BT from computer game programming to many branches of AI and Robotics. In this book, we will first give an introduction to BTs, then we describe how BTs relate to, and in many cases generalize, earlier switching structures. These ideas are then used as a foundation for a set of efficient and easy to use design principles. Properties such as safety, robustness, and efficiency are important for an autonomous system, and we describe a set of tools for formally analyzing these using a state space description of BTs. With the new analysis tools, we can formalize the descriptions of how BTs generalize earlier approaches. We also show the use of BTs in automated planning and machine learning. Finally, we describe an extended set of tools to capture the behavior of Stochastic BTs, where the outcomes of actions are described by probabilities. These tools enable the computation of both success probabilities and time to completion.

This article was written by James Le. It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix's algorithms to make movie suggestions based on movies you have watched in the past or Amazon's algorithms that recommend books based on books you have bought before. So if you want to learn more about machine learning, how do you start?

Let's talk about how Synapse AI was made. Synapse actually started out -- let's not talk about confirmation bias of being a programmer, growing up in the hacking and phreaking scene, learning networking, network security, understanding systems, having a background in electrical engineering and computation chemistry -- let's start out with: We first threw a hackathon called "Hackendo," and I was running Techendo at the time, and the San Francisco Hacker News Meetup. Techendo was our news outlet. We wanted Techendo to capture entrepreneurs and tech developers blogging about what was happening. We did that because there was a real monopoly on the pipeline of launching a product and getting media engaged to talk about a product. That was mainly owned by incubators and accelerators, and it still is -- so there still is opportunity there.

Consensus formation is investigated for multi-agent systems in which agents' beliefs are both vague and uncertain. Vagueness is represented by a third truth state meaning \emph{borderline}. This is combined with a probabilistic model of uncertainty. A belief combination operator is then proposed which exploits borderline truth values to enable agents with conflicting beliefs to reach a compromise. A number of simulation experiments are carried out in which agents apply this operator in pairwise interactions, under the bounded confidence restriction that the two agents' beliefs must be sufficiently consistent with each other before agreement can be reached. As well as studying the consensus operator in isolation we also investigate scenarios in which agents are influenced either directly or indirectly by the state of the world. For the former we conduct simulations which combine consensus formation with belief updating based on evidence. For the latter we investigate the effect of assuming that the closer an agent's beliefs are to the truth the more visible they are in the consensus building process. In all cases applying the consensus operators results in the population converging to a single shared belief which is both crisp and certain. Furthermore, simulations which combine consensus formation with evidential updating converge faster to a shared opinion which is closer to the actual state of the world than those in which beliefs are only changed as a result of directly receiving new evidence. Finally, if agent interactions are guided by belief quality measured as similarity to the true state of the world, then applying the consensus operator alone results in the population converging to a high quality shared belief.

Following Jeremy Corbyn and Theresa May's heated debate over the state of the NHS during yesterday's PMQs, some experts believe that the use of artificial intelligence could hold the key to saving the UK's NHS. AI, in particular cognitive agents that can hold a human-like conversation with the patients, is the key to rescuing the NHS and giving patients and taxpayers the level of care that they expect. Indeed, David Champeaux, director, Global Cognitive Health Solutions at IPsoft, the digital labour company suggests that AI may be the "miracle pill" for the NHS. See also: British public'would use AI' to relieve NHS pressures "The NHS is at risk of a winter of discontent," said Champeaux. "Our healthcare system is buckling under immense pressure resulting from growing demand and capacity constraints. One way to address the staff shortages is to train digital employees equipped with artificial intelligence (AI) to assist doctors and nurses and relieve them from the high volume of routine and administrative tasks and free up more time for patients."

We always keep hearing that robots or machines will replace human, and our workplaces will change dramatically. The fundamental truth is that it will, and we have an opportunity to start planning for that, but, like anything else, it's unclear exactly when the tipping point will be. Artificial Intelligence is one of the most trending topics in today's date. It is the wayof enabling a computer software to think intelligently in a manner similar to that of humans. In technical terms, it is an integrated solution of Machine Learning, Data Science, Data Mining, Predictive Analytics, Multi-agent Systems, and fast & reliable computation.