Chinese Premier Li Keqiang called on Saturday for greater global cooperation against terrorism, state media said, as the Asian giant seeks greater international support for its anti-terror fight. Speaking at an Asia-Europe summit, Li said various security challenges - conventional and unconventional - remain prominent even though those regions had remained generally stable and peaceful. "Acts of terrorism are common challenges faced by every nation. Countries should work more closely to fight terrorism, and build societies that are truly open and tolerant so to root out the soil where it grows," said Li. China has sought Western support for its own "war on terror" since the attacks in Paris last November.

The basic goal of every eCommerce company is to bring the best of offline shopping experience to the online space, by offering the consumers a seamless way to discover the products they are looking for. The avenue is taking a big leap towards becoming the facilitator of a more efficient, personalized, even automated customer journey with the introduction of cognitive technologies and the employment of'smart data'. Today, the most important area of focus in eCommerce is hyper personalization which could be facilitated only by learning consumer behaviour and making predictive analyses with the help of the huge amount of data collected from user activities on smartphones, tablets and desktops, and intelligent algorithms to process them. Machine learning and artificial intelligence are no more restricted to personal assistance technology, smartphone companies are creating. They have flouted these conventions to disrupt a much wider space with limitless possibilities. One of the areas radically transformed by AI is eCommerce.

Judgment aggregation problems form a class of collective decision-making problems represented in an abstract way, subsuming some well known problems such as voting. A collective decision can be reached in many ways, but a direct one-step aggregation of individual decisions is arguably most studied. Another way to reach collective decisions is by iterative consensus building - allowing each decision-maker to change their individual decision in response to the choices of the other agents until a consensus is reached. Iterative consensus building has so far only been studied for voting problems. Here we propose an iterative judgment aggregation algorithm, based on movements in an undirected graph, and we study for which instances it terminates with a consensus. We also compare the computational complexity of our itterative procedure with that of related judgment aggregation operators.

We consider a collaborative online learning paradigm, wherein a group of agents connected through a social network are engaged in playing a stochastic multi-armed bandit game. Each time an agent takes an action, the corresponding reward is instantaneously observed by the agent, as well as its neighbours in the social network. We perform a regret analysis of various policies in this collaborative learning setting. A key finding of this paper is that natural extensions of widely-studied single agent learning policies to the network setting need not perform well in terms of regret. In particular, we identify a class of non-altruistic and individually consistent policies, and argue by deriving regret lower bounds that they are liable to suffer a large regret in the networked setting. We also show that the learning performance can be substantially improved if the agents exploit the structure of the network, and develop a simple learning algorithm based on dominating sets of the network. Specifically, we first consider a star network, which is a common motif in hierarchical social networks, and show analytically that the hub agent can be used as an information sink to expedite learning and improve the overall regret. We also derive networkwide regret bounds for the algorithm applied to general networks. We conduct numerical experiments on a variety of networks to corroborate our analytical results.

Agents have existed already for a long time. In software engineering, they are small distributed applications that demonstrate some form of autonomous behavior. Early examples are monitoring agents, web crawlers, and chat bots. Also, today's trends in distributed and intelligent architectures can be viewed from this existing Agent perspective. Software Agents could be viewed as an evolution of, or an addition to, the (Micro)Services that are underpinning these trends.

The Association for the Advancement of Artificial Intelligence presented the 2015 Fall Symposium Series, on Thursday through Saturday, November 12-14, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the six symposia were as follows: AI for Human-Robot Interaction, Cognitive Assistance in Government and Public Sector Applications, Deceptive and Counter-Deceptive Machines, Embedded Machine Learning, Self-Confidence in Autonomous Systems, and Sequential Decision Making for Intelligent Agents. This article contains the reports from four of the symposia.

Artificial intelligence in Korea is currently prospering. The media is regularly reporting AI-enabled products such as smart advisors, personal robots, autonomous cars, and human-level intelligence machines. The IT industry is investing in deep learning and AI to maintain the global competitive edge in their services and products. The Ministry of Science, ICT, and Future Planning (MSIP) has launched new funding programs in AI and cognitive science to implement the government’s newly adopted endeavor of building a “Creative Economy” and “Software Centric Society”. However, AI was not always flourishing as it is now. Similar to the history of AI worldwide, AI research and industry in Korea have faced both the ups and downs in its history.

The Association for the Advancement of Artificial Intelligence presented the 2015 Fall Symposium Series, on Thursday through Saturday, November 12-14, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the six symposia were as follows: AI for Human-Robot Interaction, Cognitive Assistance in Government and Public Sector Applications, Deceptive and Counter-Deceptive Machines, Embedded Machine Learning, Self-Confidence in Autonomous Systems, and Sequential Decision Making for Intelligent Agents. This article contains the reports from four of the symposia.

Complexity, in my point of view, is the key for disruptive evolutions in Data Science and Machine Learning. Approaches as the one from Edgar Morin allow us to see the world through a completely different point of view, analyzing and deconstructing commom sense, leading to a completely new epistemologic view of the world and problems. An agent-based model is a model where agents are autonomous, interact with each other, iteract, follow rules for this interaction and usually we see an emergence of phenomena, many times completely disassociated from the initial condition. The system self organizes in what is called an open system, by Bertalanffy (1950). It depends upon internal changes in the system and also on environmental changes.

A new class of smart machines is emerging that can help you automate your business and make life easier for professionals by eliminating many of the routine, manual aspects of their jobs, freeing them to work on more innovative and strategic areas. Products and technologies such as intelligent agents/digital assistants, artificial intelligence (AI), virtual reality (VR) systems, intelligent software agents, expert systems and robotic office devices are likely to become more common in work environments in the years to come. A report released in February 2016 by industry research firm Research and Markets, "Artificial Intelligence Market: Global Forecast to 2020," forecasts that the AI market will grow from 419.7 million in 2014 to 5.05 billion by 2020, at a compound annual growth rate of 54 percent from 2015 to 2020. The key factors driving this growth include diversified application areas of AI, improved productivity, and increased levels of customer satisfaction, the report says. The rising demand for intelligent systems is expected to propel the growth of the market in the next five years.