Customers at Royal Bank of Scotland and NatWest may soon be sorting out issues with help from a virtual chatbot. Web-based Luvo will be able to answer simple queries such as how to order a replacement card. Designed using IBM Watson technology, the virtual agent is able to understand and learn from human interactions. In future, Luvo may be able to understand if a customer was feeling frustrated or unhappy and change its tone and actions accordingly, IBM said. The service will initially be rolled out to RBS and NatWest customers, starting in December with about 10% of RBS customers in Scotland.

SEB will be the first bank to use IPsoft's cognitive technology for customer-facing operations in the Swedish language. The artificial intelligence (AI) solution, known as Amelia, will be integrated into the front-office set-up at SEB by the end of this year. The project follows on from a successful deployment of Amelia for SEB's internal service desk, supporting 15,000 staff. In the customer-facing role, Amelia will be dealing with one million clients of SEB. "Customer service is a key differentiator. By making Amelia available to respond to queries, we enhance our customers' flexibility of receiving individualised support at a time that suits them and without any delays in response," states Rasmus Järborg, chief strategy officer at SEB.

This paper develops an active sensing method to estimate the relative weight (or trust) agents place on their neighbors' information in a social network. The model used for the regression is based on the steady state equation in the linear DeGroot model under the influence of stubborn agents, i.e., agents whose opinions are not influenced by their neighbors. This method can be viewed as a \emph{social RADAR}, where the stubborn agents excite the system and the latter can be estimated through the reverberation observed from the analysis of the agents' opinions. The social network sensing problem can be interpreted as a blind compressed sensing problem with a sparse measurement matrix. We prove that the network structure will be revealed when a sufficient number of stubborn agents independently influence a number of ordinary (non-stubborn) agents. We investigate the scenario with a deterministic or randomized DeGroot model and propose a consistent estimator of the steady states for the latter scenario. Simulation results on synthetic and real world networks support our findings.

In this work we introduce Quixote, a system that makes programming virtual agents more accessible to non-programmers by enabling these agents to be trained using the sociocultural knowledge present in stories. Quixote uses a corpus of exemplar stories to automatically engineer a reward function that is used to train virtual agents to exhibit desired behaviors using reinforcement learning. We show the effectiveness of our system with a case study conducted in a virtual environment called Robbery World that simulates a bank robbery scenario. In this case study, we use a corpus of stories crowdsourced from Amazon Mechanical Turk to guide learning. We evaluate Quixote under a variety of different conditions to determine the overall effectiveness of the system in Robbery World.

This paper examines the performance of a number of AI agents on the games included in the General Video Game Playing Competition. Through analyzing these results, the paper seeks to provide insight into the strengths and weaknesses of the current generation of video game playing algorithms. The paper also provides an analysis of the given games in terms of inherent features which define the different games. Finally, the game features are matched with AI agents, based on performance, in order to demonstrate a plausible case for algorithm portfolios as a general video game playing technique.

It is quite difficult at first to precisely define'Complexity Science'. It is a new perspective of methodology and modeling approaches that are based more on reality than assumptions. Quite simply put, Complexity Science is a new way to grasp and manage reality. It does not study systems in isolation like gambling dice or planetary motion only. It studies the complex, holistic, inter-connected reality in which we actually live such as financial stock markets, social policies, economic policies, natural catastrophes and so on.

Meet Gisele, she lives in a nice house, but it's not just a house. Thanks to a handful of connected devices and craft ai agents ruling over them, her house reacts to and learns from its occupants. Let's start with basic automation. For instance, depending on the outside light intensity, the house will adjust the light of the room in which Gisele is. She doesn't have to turn any switch on or off: she is localized inside the house by an indoor positioning system, such as a Beacon, and the connected light bulbs, such as a LIFX or Philips Hue, react accordingly, following simple logic defined in craft ai.

"An Introduction to Agent-based Modeling" is a well-written and honest look at the benefits and limitations of agent-based modeling. Agent-based modeling is a computer simulation that assigns properties to agents, and the environment they interact with. Agent-based modeling demonstrates that agents acting of their own accord will collectively self-organize into predictable macro-behavior (a concept that's similar to Adam Smith's invisible hand theory). Some of this macro-behavior will alter the environment and eco-system (a concept that's termed "emergent"). The authors are honest enough to admit that agent-based modeling is not predictive (it's too determinant on the algorithms and parameters that humans assign the model), but it can be a powerful tool for education and communication.

An artificial intelligence agent developed by two Carnegie Mellon University computer science students has proven to be the game's ultimate survivor --, outplaying both the game's built-in AI agents and human players. The students, Devendra Chaplot and Guillaume Lample, used deep-learning techniques to train the AI agent to negotiate the game's 3-D environment, still challenging after more than two decades because players must act based only on the portion of the game visible on the screen. Their work follows the groundbreaking work of Google's DeepMind, which used deep-learning methods to master two-dimensional Atari 2600 videogames and, earlier this year, defeat a world-class professional player in the board game Go. In contrast to the limited information provided in Doom, both Atari and Go give players a view of the entire playing field. "The fact that their bot could actually compete with average human beings is impressive," said Ruslan Salakhutdinov, an associate professor of machine learning who was not involved in the student project.

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for 'tricking' the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product - the classifiers - that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.