Peer review, evaluation, and selection is a fundamental aspect of modern science. Funding bodies the world over employ experts to review and select the best proposals of those submitted for funding. The problem of peer selection, however, is much more general: a professional society may want to give a subset of its members awards based on the opinions of all members; an instructor for a MOOC or online course may want to crowdsource grading; or a marketing company may select ideas from group brainstorming sessions based on peer evaluation. We make three fundamental contributions to the study of procedures or mechanisms for peer selection, a specific type of group decision-making problem, studied in computer science, economics, and political science. First, we propose a novel mechanism that is strategyproof, i.e., agents cannot benefit by reporting insincere valuations. Second, we demonstrate the effectiveness of our mechanism by a comprehensive simulation-based comparison with a suite of mechanisms found in the literature. Finally, our mechanism employs a randomized rounding technique that is of independent interest, as it solves the apportionment problem that arises in various settings where discrete resources such as parliamentary representation slots need to be divided proportionally.

There are many differing opinions on the impact of artificial intelligence (AI) on our worklives, from dazzling to dystopian. Bill Gates, for one, sees the bright side of things. "Think of all the time we spend manually organizing and performing mundane activities, from scheduling meetings to paying the bills," he writes in the foreword to Satya Nadella's new book, Hit Refresh: The Quest to Rediscover Microsoft's Soul and Imagine a Better Future for Everyone, "In the future, an AI agent will know that you are at work and have ten minutes free, and then help you accomplish something that is high on your to-do list. AI is on the verge of making our lives more productive and creative." Successful AI requires critical thinkers of the human variety.

The original article was first published on ITU Telecom World Blog. Views expressed are the author's own. In fact, it already has started – and has been running autonomously for over 8 years. It's called Multi Agent System, one of the branches of AI. It has evolved rapidly into many different forms, but most impressively one of its forms is autonomous.

In this paper, we show synchronization for a group of output passive agents that communicate with each other according to an underlying communication graph to achieve a common goal. We propose a distributed event-triggered control framework that will guarantee synchronization and considerably decrease the required communication load on the band-limited network. We define a general Byzantine attack on the event-triggered multi-agent network system and characterize its negative effects on synchronization. The Byzantine agents are capable of intelligently falsifying their data and manipulating the underlying communication graph by altering their respective control feedback weights. We introduce a decentralized detection framework and analyze its steady-state and transient performances. We propose a way of identifying individual Byzantine neighbors and a learning-based method of estimating the attack parameters. Lastly, we propose learning-based control approaches to mitigate the negative effects of the adversarial attack.

As we have seen previously with the likes of SethBling's Mar I/O videos and other examples, video games seem to be a great source for training AI neural networks. Augmented reality and machine learning are part of a collection of technologies that seem to be growing toward a point of maturity, and that will likely cause them to be intertwined for the foreseeable future. As developers, machine learning will definitely change the way we create software in the coming future. Instead of going line-by-line through code to create our next killer app, we will instead likely set the parameters and determine the training regimen for basic AI. And really, that future is likely not as far away as it may seem. In a blog post this week, Unity announced the release of the what they are calling Machine Learning Agents, or ML-agents for short.

Researchers from BYU (Brigham Young University) were successful in giving common sense to the artificial intelligence agents with the help of Wikipedia. Walk into a room, see a chair, and your brain will tell you that you can sit in it, tip it over or lift it up, but you wouldn't even consider drinking it, promoting it or unlocking it. As humans, explains BYU computer science professor David Wingate, we know intuitively that certain verbs pair naturally with certain nouns, and we also know that most verbs don't make sense when paired with random nouns. "Consider the monitor on your desk: you can look at it, you can turn it on, you can even pick it up or throw it, but you cannot impeach it, transpose it, justify it or correct it," said Wingate. "You can dethrone a king or worship him or obey him, but you cannot unlock him or calendar him or harvest him." That intuition, for the most part, doesn't exist with computer artificial intelligence agents, who are good at identifying objects but less so in knowing what to do with them.

Different agents may have different points of view. Following a popular approach in the artificial intelligence literature, this can be modelled by means of different abstract argumentation frameworks, each consisting of a set of arguments the agent is contemplating and a binary attack-relation between them. A question arising in this context is whether the diversity of views observed in such a profile of argumentation frameworks is consistent with the assumption that every individual argumentation framework is induced by a combination of, first, some basic factual attack-relation between the arguments and, second, the personal preferences of the agent concerned regarding the moral or social values the arguments under scrutiny relate to. We treat this question of rationalisability of a profile as an algorithmic problem and identify tractable and intractable cases. In doing so, we distinguish different constraints on admissible rationalisations, e.g., concerning the types of preferences used or the number of distinct values involved. We also distinguish two different semantics for rationalisability, which differ in the assumptions made on how agents treat attacks between arguments they do not report. This research agenda, bringing together ideas from abstract argumentation and social choice, is useful for understanding what types of profiles can reasonably be expected to occur in a multiagent system.

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In the smart grid, the intent is to use flexibility in demand, both to balance demand and supply as well as to resolve potential congestion. A first prominent example of such flexible demand is the charging of electric vehicles, which do not necessarily need to be charged as soon as they are plugged in. The problem of optimally scheduling the charging demand of electric vehicles within the constraints of the electricity infrastructure is called the charge scheduling problem. The models of the charging speed, horizon, and charging demand determine the computational complexity of the charge scheduling problem. For about 20 variants, we show, using a dynamic programming approach, that the problem is either in P or weakly NP-hard. We also show that about 10 variants of the problem are strongly NP-hard, presenting a potentially significant obstacle to their use in practical situations of scale.

Abstract-- Swarm systems constitute a challenging problem for reinforcement learning (RL) as the algorithm needs to learn decentralized control policies that can cope with limited local sensing and communication abilities of the agents. Although there have been recent advances of deep RL algorithms applied to multi-agent systems, learning communication protocols while simultaneously learning the behavior of the agents is still beyond the reach of deep RL algorithms. However, while it is often difficult to directly define the behavior of the agents, simple communication protocols can be defined more easily using prior knowledge about the given task. In this paper, we propose a number of simple communication protocols that can be exploited by deep reinforcement learning to find decentralized control policies in a multi-robot swarm environment. The protocols are based on histograms that encode the local neighborhood relations of the agents and can also transmit task-specific information, such as the shortest distance and direction to a desired target. In our framework, we use an adaptation of Trust Region Policy Optimization to learn complex collaborative tasks, such as formation building, building a communication link, and pushing an intruder. We evaluate our findings in a simulated 2D-physics environment, and compare the implications of different communication protocols. I. INTRODUCTION Nature provides many examples where the performance of a collective of limited beings exceeds the capabilities of one individual. Ants transport prey of the size no single ant could carry, termites build nests of up to nine meters in height, and bees are able to regulate the temperature of a hive.