In this scenario, to reduce memory consumption and possibly optimize execution times, the paper proposes novel techniques to determine an optimal indexing schema for the underlying database together with suitable body-orderings for the Datalog rules. The new approach is compared with the standard execution plans implemented in DL V over widely used ontological benchmarks. The results confirm that the memory usage can be significantly reduced without paying any cost in efficiency. This paper is under consideration in Theory and Practice of Logic Programming (TPLP). KEYWORDS: Datalog; Query Answering; Ontologies; Query-plan; Data Indexing 1 Introduction Ontological reasoning services represent fundamental features in the development of the Semantic Web. Among them, scientists are focusing their attention on the so-called ontology-based query answering (OBQA), where a Boolean query has to be evaluated against a logical theory (knowledge base) consisting of an extensional database paired with an ontology (Cal ı et al. 2009; Ortiz 2013; Amendola et al. 2018).

Hierarchical Reinforcement Learning algorithms have successfully been applied to temporal credit assignment problems with sparse reward signals. However, state-of- the-art algorithms require manual specification of sub-task structures, a sample inefficient exploration phase and lack semantic interpretability. Human infants, on the other hand, efficiently detect hierarchical substructures induced by their surroundings. In this work we propose a cognitive-inspired Reinforcement Learning architecture which uses grammar induction to identify sub-goal policies. More specifically, by treating an on-policy trajectory as a sentence sampled from the policy-conditioned language of the environment, we identify hierarchical constituents with the help of unsupervised grammatical inference. The resulting set of temporal abstractions is called action grammars (Pastra & Aloimonos, 2012) and can be used to enable efficient imitation, transfer and online learning.

As reinforcement learning continues to drive machine intelligence beyond its conventional boundary, unsubstantial practices in sparse reward environment severely limit further applications in a broader range of advanced fields. Motivated by the demand for an effective deep reinforcement learning algorithm that accommodates sparse reward environment, this paper presents Hindsight Trust Region Policy Optimization (Hindsight TRPO), a method that efficiently utilizes interactions in sparse reward conditions and maintains learning stability by restricting variance during the policy update process. Firstly, the hindsight methodology is expanded to TRPO, an advanced and efficient on-policy policy gradient method. Then, under the condition that the distributions are close, the KL-divergence is appropriately approximated by another $f$-divergence. Such approximation results in the decrease of variance during KL-divergence estimation and alleviates the instability during policy update. Experimental results on both discrete and continuous benchmark tasks demonstrate that Hindsight TRPO converges steadily and significantly faster than previous policy gradient methods. It achieves effective performances and high data-efficiency for training policies in sparse reward environments.

From the shiny corridors of BlackRock's Palo Alto laboratory, to the cramped shared workspaces of scientifically minded hedge fund start-ups, to the hallways of quantitative investing stalwarts such as Renaissance Technologies and Two Sigma, artificial intelligence (AI) is being adopted as the new temple of asset management. Even discretionary managers are starting to bring in data scientists and machine learning experts. Most attempts to apply AI so far have been in stock price forecasting. But risk managers are asking how the technology can be harnessed in their domain also. One area of exploration is the use of machine learning to replace traditional approaches to risk modelling.

Humanoid robots are a familiar trope in popular culture, but is making machines look like us a little bit creepy and even potentially dangerous? Whether it is Isaac Asimov's robotics novels, 1980s movie character Johnny 5, Hollywood's Avengers: The Age of Ultron or Channel 4's sci-fi drama Humans, there has long been a fascination in popular culture with robots becoming sentient - beings that can experience feelings and human-like consciousness. But how realistic - and desirable - is the prospect of robots that become almost indistinguishable from humans? Dr Ben Goertzel, who developed the AI software for Sophia, a social humanoid robot made by Hong Kong-based Hanson Robotics, believes robots should look like humans to help "break down suspicions and reservations people might have" about interacting with them. "You will have humanoid robots because people like them," he tells the BBC.

Things being what they are, you needn't bother with a computer to make an artificial intelligence. Truth be told, you don't require electricity. In an unprecedented piece of left-field look into, researchers from the University of Wisconsin–Madison have figured out how to make artificially wise glass that can perceive pictures with no requirement for sensors, circuits, or even a power source -- and it would one be able to day spare your phone's battery life. "We're continually contemplating how we provide vision for machines later on, and envisioning application explicit, mission-driven technologies," scientist Zongfu Yu said in an official statement. In a proof-of-idea concentrate published on Monday in the diary Photonics Research, the analysts portray how they made a sheet of "savvy" glass that could distinguish manually written digits.

Things being what they are, you needn't bother with a computer to make an artificial intelligence. Truth be told, you don't require electricity. In an unprecedented piece of left-field look into, researchers from the University of Wisconsin–Madison have figured out how to make artificially wise glass that can perceive pictures with no requirement for sensors, circuits, or even a power source -- and it would one be able to day spare your phone's battery life. "We're continually contemplating how we provide vision for machines later on, and envisioning application explicit, mission-driven technologies," scientist Zongfu Yu said in an official statement. In a proof-of-idea concentrate published on Monday in the diary Photonics Research, the analysts portray how they made a sheet of "savvy" glass that could distinguish manually written digits.

We've seen a big push in recent months to solve AI's "big data problem." And some interesting breakthroughs have begun to emerge that could make AI accessible to many more businesses and organizations. What is the big data problem? It's the challenge of getting enough data to enable deep learning, a very popular and promising AI technique that allows machines to find relationships and patterns in data by themselves. If you change'cat' to'customer,' you can see why many companies are eager to test-drive this technology.)

Indonesian e-commerce unicorn Tokopedia is investing in two logistics companies and using artificial intelligence to predict buying behaviour as part of an effort to speed delivery and slash shipping costs in the world's biggest archipelago. Tokopedia founder and chief executive William Tanuwijaya also said the firm is in final talks to invest in an agritech startup that works with farmers and has been buying stakes in smaller marketplaces offering everything from wedding services to secondhand phones. "The customer in Tokopedia is going to be able to buy directly from farmers and fishermen," Tanuwijaya told Reuters in an interview. Tanuwijaya expects Tokopedia to lift the proportion of one-day deliveries to 95% from the current 65%, while cutting costs for merchants in a country of 17,000 islands where logistics costs can be exorbitant. Backed by $2 billion in funding from investors including SoftBank Group Corp's Vision Fund and Alibaba, Tokopedia aims to create a "super-ecosystem" with a wide range of services, including lending and payment services for both consumer and merchants.

As reported by PwC and CB Insights' MoneyTree Report, the overall growth of Venture Capital investments activity in the United States reached $100 billion in 2018 - a truly watershed moment. And as U.S. Senator, Everett Dirksen once said: "A billion here, a billion there, pretty soon, you're talking real money"... So, since we are talking about so many billions, it's more important than ever for the VC community to make smarter investments. Instead of relying on startup founder's charisma, or the quality of his/her pitch deck, well-trained AI can help to predict investment's success probability much faster than going through manual analysis, and with far fewer emotions while at it. All business plans should meet the same rigorous and unbiased investment criteria - and this is where AI comes handy. It can be used all across the board: to discover, evaluate and support VC's investments! For example blogs, posts and other social media sources can be successfully scanned using Natural Language Processing - to identify early-stage companies searching for CVC/IVC investors. And since AI can be constantly fed with new data sets and new information, it can follow the emerging trends - accurately.