We consider a problem of placing generators of rewards to be collected by randomly moving agents in a network. In many settings, the precise mobility pattern may be one of several possible, based on parameters outside our control, such as weather conditions. The placement should be robust to this uncertainty, to gain a competent total reward across possible networks. To study such scenarios, we introduce the Robust Reward Placement problem (RRP). Agents move randomly by a Markovian Mobility Model with a predetermined set of locations whose connectivity is chosen adversarially from a known set $\Pi$ of candidates. We aim to select a set of reward states within a budget that maximizes the minimum ratio, among all candidates in $\Pi$, of the collected total reward over the optimal collectable reward under the same candidate. We prove that RRP is NP-hard and inapproximable, and develop $\Psi$-Saturate, a pseudo-polynomial time algorithm that achieves an $\epsilon$-additive approximation by exceeding the budget constraint by a factor that scales as $O(\ln |\Pi|/\epsilon)$. In addition, we present several heuristics, most prominently one inspired by a dynamic programming algorithm for the max-min 0-1 KNAPSACK problem. We corroborate our theoretical analysis with an experimental evaluation on synthetic and real data.

Progress in fields of machine learning and adversarial planning has benefited significantly from benchmark domains, from checkers and the classic UCI data sets to Go and Diplomacy. In sequential decision-making, agent evaluation has largely been restricted to few interactions against experts, with the aim to reach some desired level of performance (e.g. beating a human professional player). We propose a benchmark for multiagent learning based on repeated play of the simple game Rock, Paper, Scissors along with a population of forty-three tournament entries, some of which are intentionally sub-optimal. We describe metrics to measure the quality of agents based both on average returns and exploitability. We then show that several RL, online learning, and language model approaches can learn good counter-strategies and generalize well, but ultimately lose to the top-performing bots, creating an opportunity for research in multiagent learning.

What to buy the tech enthusiast in your life? Here are some ideas – from smart speakers and games consoles to smartwatches and headphones. With Christmas rapidly approaching, time is running out to buy the big-ticket items, so we have also included some instant-delivery gifts for last-minute purchases. Google's cheapest little smart speaker looks like a pincushion and is great for playing the radio, answering questions and controlling smart home devices. It comes in four colours and can even be wall mounted. The best-sounding smart speaker under £100 is often available for far less in sales.

Amazon's fourth-generation Echo Alexa smart speaker is a complete redesign in form and audio, with the popular device transformed into a ball of sound. The Echo costs £89.99 and is Amazon's mid-range speaker, sitting above the £49.99 Echo Dot and below the £189.99 The first smart speaker on the market, the original Echo set the standard in 2014 with a tall cylinder shape and 360-degree audio – resembling a Pringles can with a speaker in it. Six years later and on its fourth generation, Amazon has now broken the mould with a new spherical shape and directional audio. The plastic and mesh fabric ball has one woofer and two tweeter speakers producing directional, stereo sound – and is designed to sit in the corner of a room rather than the centre of it.

After almost six years on the market, smart speakers now come in a variety of sizes, shapes, capabilities and prices. Whether you want a cheap speaker to keep the kids entertained, one that doubles as a digital photo frame or one that sounds so good you'll want to yell "turn it up to 11", here's a quick guide to the best on the market. Google's Assistant is the best voice system on the market. It has better understanding than rivals, an enormous range of knowledge and – importantly – the ability to choose between male and female voices, even on a user-by-user basis as Google can distinguish between the individuals giving instructions. The Nest Mini is the second generation of Google's smallest and cheapest smart speaker.

Amazon has a new twist on its popular cut-price Echo Dot smart speaker, now setting its sights squarely on your beleaguered bedside alarm clock with a new LED display embedded in the side. The Echo Dot with Clock is one of those true Ronseal products - it says what it does on the tin. It is literally the same as the excellent third-generation Echo Dot, but is only available in white and has a white LED display showing the time peeking through the fabric side. It's formally priced at £60 – £10 more than the regular Echo Dot – but is frequently discounted to about half that. You get the same four-way buttons on the top: volume up and down, mute for the microphones and an action button.

Japan's On-Art Corp's CEO Kazuya Kanemaru poses with his company's eight metre tall dinosaur-shaped mechanical suit robot'TRX03' and other robots during a demonstration in Tokyo, Japan Japan's On-Art Corp's eight metre tall dinosaur-shaped mechanical suit robot'TRX03' performs during its unveiling in Tokyo, Japan Singulato Motors co-founder and CEO Shen Haiyin poses in his company's concept car Tigercar P0 at a workshop in Beijing, China A picture shows Singulato Motors' concept car Tigercar P0 at a workshop in Beijing, China Connected company president Shigeki Tomoyama addresses a press briefing as he elaborates on Toyota's "connected strategy" in Tokyo. A Toyota Motors employee demonstrates a smartphone app with the company's pocket plug-in hybrid (PHV) service on the cockpit of the latest Prius hybrid vehicle during Toyota's "connected strategy" press briefing in Tokyo An employee shows a Samsung Electronics' Gear S3 Classic during Korea Electronics Show 2016 in Seoul, South Korea Visitors experience Samsung Electronics' Gear VR during the Korea Electronics Grand Fair at an exhibition hall in Seoul, South Korea Amy Rimmer, Research Engineer at Jaguar Land Rover, demonstrates the car manufacturer's Advanced Highway Assist in a Range Rover, which drives the vehicle, overtakes and can detect vehicles in the blind spot, during the first demonstrations of the UK Autodrive Project at HORIBA MIRA Proving Ground in Nuneaton, Warwickshire Chris Burbridge, Autonomous Driving Software Engineer for Tata Motors European Technical Centre, demonstrates the car manufacturer's GLOSA V2X functionality, which is connected to the traffic lights and shares information with the driver, during the first demonstrations of the UK Autodrive Project at HORIBA MIRA Proving Ground in Nuneaton, Warwickshire In its facilities, JAXA develop satellites and analyse their observation data, train astronauts for utilization in the Japanese Experiment Module'Kibo' of the International Space Station (ISS) and develop launch vehicles The robot developed by Seed Solutions sings and dances to the music during the Japan Robot Week 2016 at Tokyo Big Sight. If those are out of your price range, you can find more Amazon Prime Day headphones deals here. The Sony ILCE6300LB Compact System camera is available for £869 (RRP £1,250), the Sony ILCE6300B Compact System camera is available for £749 (RRP £1,070) and the Sony DSCRX10M3 4K Premium Digital Bridge Mk3 camera is available for £1,129 (RRP £1,549).

What is a systematic way to efficiently apply a wide spectrum of advanced ML programs to industrial scale problems, using Big Models (up to 100s of billions of parameters) on Big Data (up to terabytes or petabytes)? Modern parallelization strategies employ fine-grained operations and scheduling beyond the classic bulk-synchronous processing paradigm popularized by MapReduce, or even specialized graph-based execution that relies on graph representations of ML programs. The variety of approaches tends to pull systems and algorithms design in different directions, and it remains difficult to find a universal platform applicable to a wide range of ML programs at scale. We propose a general-purpose framework that systematically addresses data- and model-parallel challenges in large-scale ML, by observing that many ML programs are fundamentally optimization-centric and admit error-tolerant, iterative-convergent algorithmic solutions. This presents unique opportunities for an integrative system design, such as bounded-error network synchronization and dynamic scheduling based on ML program structure. We demonstrate the efficacy of these system designs versus well-known implementations of modern ML algorithms, allowing ML programs to run in much less time and at considerably larger model sizes, even on modestly-sized compute clusters.