Behavior Trees (BTs) were invented as a tool to enable modular AI in computer games, but have received an increasing amount of attention in the robotics community in the last decade. With rising demands on agent AI complexity, game programmers found that the Finite State Machines (FSM) that they used scaled poorly and were difficult to extend, adapt and reuse. In BTs, the state transition logic is not dispersed across the individual states, but organized in a hierarchical tree structure, with the states as leaves. This has a significant effect on modularity, which in turn simplifies both synthesis and analysis by humans and algorithms alike. These advantages are needed not only in game AI design, but also in robotics, as is evident from the research being done. In this paper we present a comprehensive survey of the topic of BTs in Artificial Intelligence and Robotic applications. The existing literature is described and categorized based on methods, application areas and contributions, and the paper is concluded with a list of open research challenges.
Artificial intelligence (AI) has the potential to deliver significant social and economic benefits, including reducing accidental deaths and injuries, making new scientific discoveries, and increasing productivity. However, an increasing number of activists, scholars, and pundits see AI as inherently risky, creating substantial negative impacts such as eliminating jobs, eroding personal liberties, and reducing human intelligence. Some even see AI as dehumanizing, dystopian, and a threat to humanity. As such, the world is dividing into two camps regarding AI: those who support the technology and those who oppose it. Unfortunately, the latter camp is increasingly dominating AI discussions, not just in the United States, but in many nations around the world. There should be no doubt that nations that tilt toward fear rather than optimism are more likely to put in place policies and practices that limit AI development and adoption, which will hurt their economic growth, social ...
Due to the proliferation of smart devices and emerging applications, many next-generation technologies have been paid for the development of wireless networks. Even though commercial 5G has just been widely deployed in some countries, there have been initial efforts from academia and industrial communities for 6G systems. In such a network, a very large number of devices and applications are emerged, along with heterogeneity of technologies, architectures, mobile data, etc., and optimizing such a network is of utmost importance. Besides convex optimization and game theory, swarm intelligence (SI) has recently appeared as a promising optimization tool for wireless networks. As a new subdivision of artificial intelligence, SI is inspired by the collective behaviors of societies of biological species. In SI, simple agents with limited capabilities would achieve intelligent strategies for high-dimensional and challenging problems, so it has recently found many applications in next-generation wireless networks (NGN). However, researchers may not be completely aware of the full potential of SI techniques. In this work, our primary focus will be the integration of these two domains: NGN and SI. Firstly, we provide an overview of SI techniques from fundamental concepts to well-known optimizers. Secondly, we review the applications of SI to settle emerging issues in NGN, including spectrum management and resource allocation, wireless caching and edge computing, network security, and several other miscellaneous issues. Finally, we highlight open challenges and issues in the literature, and introduce some interesting directions for future research.
The rise of e-commerce over the last 10 years or so has forced retailers to adapt to the changes demanded by consumers. E-commerce growth continues to accelerate and outpace growth in the brick-and-mortar channel, and online sales accounted for almost 20% of total US sales this holiday season, based on preliminary estimates. In addition, department stores have offered discounts and promotions as a key tool to drive demand and bring consumers into stores. Over time, this strategy can dilute a store's brand and leave stores looking picked through. Also, it trains consumers to wait for discounts instead of buying products at full price. There has been a significant number of store closures in the last few years, and we expect that to accelerate in 2017 and in the following few years. As the department store channel shrinks, and more brands fight for less space, we think brands will need to be more creative, flexible and diversified in their approaches. One way brands can disrupt the more traditional wholesale channel without taking on the significant real estate risk that comes with opening their own stores is to open pop-up stores. With pop-ups, brands have complete creative control of the brand experience and how their messaging is communicated to consumers. They can tell the story they want to tell and explain in their own voice what the brand stands for. In some cases, brands use pop-ups more as an advertising tool than as a place to transact commerce. These kinds of pop-ups usually offer some kind of special experience to draw consumers into the store. Pop-ups can also be set up in locations other than malls, allowing brands to reach their target customers where they are. Retailers and brands can also use pop-ups to test the waters in the most expensive shopping areas, often at discounted rents, while landlords can use the temporary stores to show off the space to prospective long-term tenants. Mall operators are receptive to pop-ups, as they bring something new and unique to consumers. Real estate firm Related Companies has used pop-up shops at the Time Warner Center in New York City to provide a fresh feel and add variety for consumers.