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.

The field of Multi-Agent System (MAS) is an active area of research within Artificial Intelligence, with an increasingly important impact in industrial and other real-world applications. Within a MAS, autonomous agents interact to pursue personal interests and/or to achieve common objectives. Distributed Constraint Optimization Problems (DCOPs) have emerged as one of the prominent agent architectures to govern the agents' autonomous behavior, where both algorithms and communication models are driven by the structure of the specific problem. During the last decade, several extensions to the DCOP model have enabled them to support MAS in complex, real-time, and uncertain environments. This survey aims at providing an overview of the DCOP model, giving a classification of its multiple extensions and addressing both resolution methods and applications that find a natural mapping within each class of DCOPs. The proposed classification suggests several future perspectives for DCOP extensions, and identifies challenges in the design of efficient resolution algorithms, possibly through the adaptation of strategies from different areas.

We consider the problem of accelerating distributed optimization in multi-agent networks by sequentially adding edges. Specifically, we extend the distributed dual averaging (DDA) subgradient algorithm to evolving networks of growing connectivity and analyze the corresponding improvement in convergence rate. It is known that the convergence rate of DDA is influenced by the algebraic connectivity of the underlying network, where better connectivity leads to faster convergence. However, the impact of network topology design on the convergence rate of DDA has not been fully understood. In this paper, we begin by designing network topologies via edge selection and scheduling. For edge selection, we determine the best set of candidate edges that achieves the optimal tradeoff between the growth of network connectivity and the usage of network resources. The dynamics of network evolution is then incurred by edge scheduling. Further, we provide a tractable approach to analyze the improvement in the convergence rate of DDA induced by the growth of network connectivity. Our analysis reveals the connection between network topology design and the convergence rate of DDA, and provides quantitative evaluation of DDA acceleration for distributed optimization that is absent in the existing analysis. Lastly, numerical experiments show that DDA can be significantly accelerated using a sequence of well-designed networks, and our theoretical predictions are well matched to its empirical convergence behavior.

If you're placing any Golden Globes bets, then maybe you might want to consider what the latest A.I. predictions are saying. In both Golden Globes Best Picture categories -- comedy and drama -- Unanimous A.I. and applied their unique systems to forecast possible winners. And it looks like this year, you can place a lot of faith in a certain sea monster cleaning up. The Shape of Water is looking good in the categories of Best Actress, but also Best Picture, too. On Friday, in order to forecast possible outcomes in a variety of categories at the Golden Globes, Unanimous A.I. used what's known as "swarm intelligence."

The Sixth International Conference on Enterprise Information Systems (ICEIS) was held in Porto, Portugal; previous venues were in Spain, France, and the United Kingdom. Since its inception in 1999, ICEIS has grown steadily, and is now one of the largest international conferences in the area of information systems. In 2004, more than 600 papers were submitted to the conference and its ten satellite workshops. One of the interesting features of this conference is the high number of invited speakers. In 2004, eighteen keynote speakers were featured at ICEIS and its workshops.