Interest in emergent communication has recently surged in Machine Learning. The focus of this interest has largely been either on investigating the properties of the learned protocol or on utilizing emergent communication to better solve problems that already have a viable solution. Here, we consider self-driving cars coordinating with each other and focus on how communication influences the agents' collective behavior. Our main result is that communication helps (most) with adverse conditions.

Vital for the building and maintaining of successful business relationships, networking is now easier and more effective than ever before. Invacio Network is a relevancy-driven web desktop that offers a suite of features designed to help you to build meaningful business and personal relationships with the right people, whoever and wherever they may be... Powerful yet extremely elegant, Network gives immediate access to a broad range of Invacio products and services, including Data, Agnes, Invmail, Alise and Consumer, as well as up to the minute content that is tailored exactly to your use by the work of our groundbreaking multi-agent system AI. Whether you wish to follow company and market tickers and charting with one click or to get real time information on the things that matter to you from the entire web, there is never sponsored content, never the clutter of advertising ...only the information you need when you need it, whatever your purpose. The hassle is taken out of arranging business meetings with our Meet Me feature. Simply choose to broadcast the location of your next trip and let other users know when you're in their area seeking business meetings or perhaps even just a friendly face to have dinner with.

The spread of autonomous systems into safety-critical areas has increased the demand for their formal verification, not only due to stronger certification requirements but also to public uncertainty over these new technologies. However, the complex nature of such systems, for example, the intricate combination of discrete and continuous aspects, ensures that whole system verification is often infeasible. This motivates the need for novel analysis approaches that modularise the problem, allowing us to restrict our analysis to one particular aspect of the system while abstracting away from others. For instance, while verifying the real-time properties of an autonomous system we might hide the details of the internal decision-making components. In this paper we describe verification of a range of properties across distinct dimesnions on a practical hybrid agent architecture. This allows us to verify the autonomous decision-making, real-time aspects, and spatial aspects of an autonomous vehicle platooning system. This modular approach also illustrates how both algorithmic and deductive verification techniques can be applied for the analysis of different system subcomponents.

AI will augment people's capabilities but won't take all jobs. However, there will be socioeconomic upheaval, warns PwC's authority on AI, Anand Rao. Anand Rao is PwC global leader for artificial intelligence (AI) and is the consulting giant's innovation lead for the US analytics practice. Rao has 24 years of industry and consulting experience, helping senior executives to structure, solve and manage critical issues facing their organisations. He has worked extensively on business, technology and analytics issues across a wide range of industry sectors including financial services, healthcare, telecommunications, aerospace and defence, across US, Europe, Asia and Australia.

A common mechanism to assess trust in crowdworkers is to have them answer gold tasks. However, assigning gold tasks to all workers reduces the efficiency of the platform. We propose a mechanism that exploits transitivity so that a worker can be certified as trusted by other trusted workers who solve common tasks. Thus, trust can be derived from a smaller number of gold tasks assignment through multiple layers of peer relationship among the workers, a model we call deep trust. We use the derived trust to incentivize workers for high quality work and show that the resulting mechanism is dominant strategy incentive compatible. We also show that the mechanism satisfies a notion of fairness in that the trust assessment (and thus the reward) of a worker in the limit is independent of the quality of other workers.

As learning algorithms are increasingly deployed in markets and other competitive environments, understanding their dynamics is becoming increasingly important. We study the limiting behavior of competitive agents employing gradient-based learning algorithms. Specifically, we introduce a general framework for competitive gradient-based learning that encompasses a wide breadth of learning algorithms including policy gradient reinforcement learning, gradient based bandits, and certain online convex optimization algorithms. We show that unlike the single agent case, gradient learning schemes in competitive settings do not necessarily correspond to gradient flows and, hence, it is possible for limiting behaviors like periodic orbits to exist. We introduce a new class of games, Morse-Smale games, that correspond to gradient-like flows. We provide guarantees that competitive gradient-based learning algorithms (both in the full information and gradient-free settings) avoid linearly unstable critical points (i.e. strict saddle points and unstable limit cycles). Since generic local Nash equilibria are not unstable critical points---that is, in a formal mathematical sense, almost all Nash equilibria are not strict saddles---these results imply that gradient-based learning almost surely does not get stuck at critical points that do not correspond to Nash equilibria. For Morse-Smale games, we show that competitive gradient learning converges to linearly stable cycles (which includes stable Nash equilibria) almost surely. Finally, we specialize these results to commonly used multi-agent learning algorithms and provide illustrative examples that demonstrate the wide range of limiting behaviors competitive gradient learning exhibits.

Recently, multiagent deep reinforcement learning (DRL) has received increasingly wide attention. Existing multiagent DRL algorithms are inefficient when facing with the non-stationarity due to agents update their policies simultaneously in stochastic cooperative environments. This paper extends the recently proposed weighted double estimator to the multiagent domain and propose a multiagent DRL framework, named weighted double deep Q-network (WDDQN). By utilizing the weighted double estimator and the deep neural network, WDDQN can not only reduce the bias effectively but also be extended to scenarios with raw visual inputs. To achieve efficient cooperation in the multiagent domain, we introduce the lenient reward network and the scheduled replay strategy. Experiments show that the WDDQN outperforms the existing DRL and multiaent DRL algorithms, i.e., double DQN and lenient Q-learning, in terms of the average reward and the convergence rate in stochastic cooperative environments.

The trio is considered the best players in the world right now with Messi and Neymar playing alongside each other for Barcelona from 2013 to 2017 in a spell that saw the club win two La Liga titles, three Copa del Reys, one Champions League crown and the FIFA Club World Cup. However, Neymar shockingly made the move from Barcelona to Paris Saint-Germain last summer after the oil-rich Ligue 1 side activated his €222 million ($273.7 million) release clause in what is still the world record transfer deal. It is highly believed that the Brazilian did not move for money but rather, the chance to play for a side built around him rather than Messi as with his current injury aside, he has flourished this season with 29 goals and 19 assists in 30 games in all competitions with PSG requiring just four points from their last six games to seal a fifth Ligue 1 title in six years. But, there are rumors that the 25-year-old is unhappy in Paris with speculation rising that he could return to La Liga for Barcelona or even rivals Real Madrid where he would be teammates with Ronaldo. There were also reports that Zahavi, who acted as an intermediary for Neymar's move last summer, accompanied a PSG delegation to Brazil last month to check on his injury and determine his value for another potential transfer.

Creating strong agents for games with more than two players is a major open problem in AI. Common approaches are based on approximating game-theoretic solution concepts such as Nash equilibrium, which have strong theoretical guarantees in two-player zero-sum games, but no guarantees in non-zero-sum games or in games with more than two players. We describe an agent that is able to defeat a variety of realistic opponents using an exact Nash equilibrium strategy in a 3-player imperfect-information game. This shows that, despite a lack of theoretical guarantees, agents based on Nash equilibrium strategies can be successful in multiplayer games after all.

Numerous, artificially intelligent, networked things will populate the battlefield of the future, operating in close collaboration with human warfighters, and fighting as teams in highly adversarial environments. This paper explores the characteristics, capabilities and intelligence required of such a network of intelligent things and humans - Internet of Battle Things (IOBT). It will experience unique challenges that are not yet well addressed by the current generation of AI and machine learning.