This work introduces two strategies for training network classifiers with heterogeneous agents. One strategy promotes global smoothing over the graph and a second strategy promotes local smoothing over neighbourhoods. It is assumed that the feature sizes can vary from one agent to another, with some agents observing insufficient attributes to be able to make reliable decisions on their own. As a result, cooperation with neighbours is necessary. However, due to the fact that the feature dimensions are different across the agents, their classifier dimensions will also be different. This means that cooperation cannot rely on combining the classifier parameters. We instead propose smoothing the outputs of the classifiers, which are the predicted labels. By doing so, the dynamics that describes the evolution of the network classifier becomes more challenging than usual because the classifier parameters end up appearing as part of the regularization term as well. We illustrate performance by means of computer simulations.

Reasoning about observed effects and their causes is important in multi-agent contexts. While there has been much work on causality from an objective standpoint, causality from the point of view of some particular agent has received much less attention. In this paper, we address this issue by incorporating an epistemic dimension to an existing formal model of causality. We define what it means for an agent to know the causes of an effect. Then using a counterexample, we prove that epistemic causality is a different notion from its objective counterpart. 1 Introduction Research on actual causality involves finding in a given narrative (trace) the event that caused an effect. Pearl [25, 26] was a pioneer to lead a computational enquiry in actual causality. The research was later continued by Halpern and Pearl [12, 15] and others [8, 17, 18, 13, 14]. Unfortunately, as argued by Glymour et al. [9], most of these accounts are developed by analyzing a handful of simple examples, and then validated relative to our intuition for these examples, a process which G oßler et al. [11] referred to as TEGAR (i.e. As such, even after multiple revisions, these definitions continue to suffer from various conceptual problems such as the early preemption problem and the over-determination problem. For instance, despite claims to the contrary, the definitions given in [14] suffer from the problem of preemption, which occurs when two competing events try to achieve the same effect and the latter of these fails to do so as the earlier one has already achieved the effect (see [31] and [4] for a discussion). In an attempt to address these issues, Batusov and Soutchanski [2, 3] recently proposed a new definition of actual causality that is based on a well developed and expressive formalization of actions and change, namely the situation calculus [23, 27]. The definition is derived from first principles and does not follow a TEGAR scheme.

Our research aims at developing intelligent systems to reduce the transportation-related energy expenditure of a large city by influencing individual behavior. We introduce Copter - an intelligent travel assistant that evaluates multi-modal travel alternatives to find a plan that is acceptable to a person given their context and preferences. We propose a formulation for acceptable planning that brings together ideas from AI, machine learning, and economics. This formulation has been incorporated in Copter that produces acceptable plans in real-time. We adopt a novel empirical evaluation framework that combines human decision data with a high fidelity multi-modal transportation simulation to demonstrate a 4% energy reduction and 20% delay reduction in a realistic deployment scenario in Los Angeles, California, USA. This article is part of the special track on AI and Society.

WIRE)--RiskSense, Inc., pioneering risk-based vulnerability management and prioritization, today announced that its CEO, Dr. Srinivas Mukkamala will lead an expert panel at the SINET Showcase conference in Washington, DC on November 7, 2019 on the impact of bias in AI-driven security systems. Dr. Srinivas Mukkamala, co-founder and CEO of RiskSense, is a recognized expert on artificial intelligence (AI) and neural networks. He was part of a think tank that collaborated with the U.S. Department of Defense and U.S. Intelligence Community to apply these concepts against cybersecurity problems. Dr. Mukkamala was also a lead researcher for CACTUS (Computational Analysis of Cyber Terrorism against the U.S.) and holds a patent on Intelligent Agents for Distributed Intrusion Detection System and Method of Practicing. Artificial intelligence and machine learning are increasingly being used and trusted by organizations to automate security threat detection.

Newswire) GBT Technologies Inc. (OTCPINK: GTCH) ("GBT", or the "Company"), a company specializing in the development of Internet of Things (IoT) and Artificial Intelligence (AI) enabled networking and tracking technologies, including its GopherInsight wireless mesh network technology platform and its Avant! AI, for both mobile and fixed solutions, announced that it is now adding the first elements of cognitive features within its AI expert agent. The agent now includes feedback features, i.e. "thumbs up" and "thumbs down", that work with the artificial neural network mechanism to learn and improve answers' accuracy and their relationship to the topic. The user feedback is fed into the Avant! RNN (Recurrent Neural Network), which synthesizes data from various information sources, weighing and comparing the feedback to the answer context to provide the best, most accurate answers.

We study a wide class of non-convex non-concave min-max games that generalizes over standard bilinear zero-sum games. In this class, players control the inputs of a smooth function whose output is being applied to a bilinear zero-sum game. This class of games is motivated by the indirect nature of the competition in Generative Adversarial Networks, where players control the parameters of a neural network while the actual competition happens between the distributions that the generator and discriminator capture. We establish theoretically, that depending on the specific instance of the problem gradient-descent-ascent dynamics can exhibit a variety of behaviors antithetical to convergence to the game theoretically meaningful min-max solution. Specifically, different forms of recurrent behavior (including periodicity and Poincar\'e recurrence) are possible as well as convergence to spurious (non-min-max) equilibria for a positive measure of initial conditions. At the technical level, our analysis combines tools from optimization theory, game theory and dynamical systems.

The rise of'deep learning' has caused a lot of excitement around the revolutionary capabilities of these artificially intelligent agents. But it's also raised fear and suspicion about what exactly is going on inside each algorithm. One way for us to gain some understanding of our silicon-based friends (or foes?) is for them to disclose their framework of decision-making in a way that we humans can understand – by using the concept of personality. My research explores how some of these deep learning agents can be better understood through their'personalities' – like whether they are'greedy', 'selfish' or'prudent'. We are now at the dawn of a new era in AI technology – a so-called fourth industrial revolution that will reshape every industry.

What to know about SingularityNET (AGI)? Blockchain technology has become one of the most in-demand technologies worldwide. Among the innovations that prove its advancement is the launching of SingularityNet. Many will confuse it with a typical marketplace, but SingularityNet is a decentralized marketplace for Artificial Intelligence (AI). The businesses associated with AI are increasing daily; however, there's a significant difference between the people developing AI tools (researchers and academics) and the businesses that want to make use of the technology for specific needs.

Will the Third World War be fought by robots? This short film is a light-hearted comedy that aims to trigger an interesting discussion and reflexion on the terrifying killer-robot stories that increasingly fill us with dread when we read the news headlines. The fictional scenario takes inspiration from current scientific research and describes a future where robots are asked by humans to join the war. Robots are divided, sparking protests in robot society... will robots join the conflict or will they refuse to be employed in human warfare? Food for thought for engineers, roboticists and anyone imagining what the upcoming robot revolution could look like. We let robots pop on camera to tell a story, taking on the role of actors playing in the film, instructed through code on how to "act" for each scene.

For seven years, AI researchers have been struggling with an unusual challenge: shooting cartoon birds at cartoon pigs. An annual competition tests their ability to craft an AI agent that can play the popular video game Angry Birds. This month two researchers posted a paper on arXiv.org It's an example of the kind of weird obstacles that all AI researchers face as they attempt to adapt cutting-edge technologies to some very human endeavors. Teams around the world are tackling much more sophisticated problems, persevering to overcome the obstacles on the path to our shiny technology-enhanced future.