The theory of abstract argumentation frameworks (afs) has, in the main, focused on finite structures, though there are many significant contexts where argumentation can be regarded as a process involving infinite objects. To address this limitation, in this paper we propose a novel approach for describing infinite afs using tools from formal language theory. In particular, the possibly infinite set of arguments is specified through the language recognized by a deterministic finite automaton while a suitable formalism, called attack expression, is introduced to describe the relation of attack between arguments. The proposed approach is shown to satisfy some desirable properties which can not be achieved through other "naive" uses of formal languages. In particular, the approach is shown to be expressive enough to capture (besides any arbitrary finite structure) a large variety of infinite afs including two major examples from previous literature and two sample cases from the domains of multi-agent negotiation and ambient intelligence. On the computational side, we show that several decision and construction problems which are known to be polynomial time solvable in finite afs are decidable in the context of the proposed formalism and we provide the relevant algorithms. Moreover we obtain additional results concerning the case of finitary afs.

The issue of representing attacks to attacks in argumentation is receiving an increasing attention as a useful conceptual modelling tool in several contexts. In this paper we present AFRA, a formalism encompassing unlimited recursive attacks within argumentation frameworks. AFRA satisfies the basic requirements of definition simplicity and rigorous compatibility with Dung's theory of argumentation. This paper provides a complete development of the AFRA formalism complemented by illustrative examples and a detailed comparison with other recursive attack formalizations.

Assistive robotics and particularly robot coaches may be very helpful for rehabilitation healthcare. In this context, we propose a method based on Gaussian Process Latent Variable Model (GP-LVM) to transfer knowledge between a physiotherapist, a robot coach and a patient. Our model is able to map visual human body features to robot data in order to facilitate the robot learning and imitation. In addition , we propose to extend the model to adapt robots' understanding to patient's physical limitations during the assessment of rehabilitation exercises. Experimental evaluation demonstrates promising results for both robot imitation and model adaptation according to the patients' limitations.

Good parameter settings are crucial to achieve high performance in many areas of artificial intelligence (AI), such as propositional satisfiability solving, AI planning, scheduling, and machine learning (in particular deep learning). Automated algorithm configuration methods have recently received much attention in the AI community since they replace tedious, irreproducible and error-prone manual parameter tuning and can lead to new state-of-the-art performance. However, practical applications of algorithm configuration are prone to several (often subtle) pitfalls in the experimental design that can render the procedure ineffective. We identify several common issues and propose best practices for avoiding them. As one possibility for automatically handling as many of these as possible, we also propose a tool called GenericWrapper4AC.

Thanks to Amazon, the world has a nifty new cautionary tale about the perils of teaching computers to make human decisions. According to a Reuters report published Wednesday, the tech giant decided last year to abandon an "experimental hiring tool" that used artificial intelligence to rate job candidates, in part because it discriminated against women. Recruiters reportedly looked at the recommendations the program spat out while searching for talent, "but never relied solely on those rankings." The misadventure began in 2014, when a group of Amazon engineers in Scotland set out to mechanize the company's head-hunting process, by creating a program that would scour the Internet for worthwhile job candidates (and presumably save Amazon's HR staff some soul crushing hours clicking around LinkedIn). "Everyone wanted this holy grail," a source told Reuters.

Great Ormond Street Hospital (GOSH) has paired up with Cambridge-based technology giant Arm to develop smart?person recognition?

Dutch scientists have developed robot bees which could help pollinate plants without the use of insects. Researchers at Delft University of Technology in the Netherlands believe they may have solved the problem of climate change or pesticides killing off the creatures. The DelFly Nimble's wings beat at 17 times per second to power the robot at speeds over 15 miles per hour (25kph). However, they share an uncanny resemblance to robot bees that are hacked and turned into killing machines in the popular science fiction series Black Mirror. It uses off-the-shelf components, making it cheap to build, and scientists say it could be used in a host of real-world applications.

An NHS Trust has started using robots to make routine referrals as part of a drive to cut costs and boost efficiency. East Suffolk and North Essex NHS Foundation Trust (ESNEFT) is testing the ability of new artificial intelligence technology to conduct administrative work, such as submitting scan and blood test results from GPs to hospital consultants. It claims the system is eight times more productive than using human staff. The system has been in operation at Ipswich Hospital for the past three months and has saved staff more than 570 hours of work. The Trust claims the system will save them £220,000 in the next 9 months.

The deal, announced during a developers' conference in Munich, underscores the importance of computing power even for partially autonomous systems, which are not designed for full self-driving but offer drivers assistance, such as lane keeping or emergency braking.

With new software it calls RAPIDS, Nvidia is hoping to move beyond the fast-growing but relatively small market for artificial intelligence chips. It's aiming for the much larger market for chips that do basic business data crunching such as forecasting inventories. Nvidia announced the software at a conference in Germany.