Researchers at Ecole Centrale de Lyon have recently devised a new developmental framework inspired by the long-term memory and reasoning mechanisms of humans. This framework, outlined in a paper presented at IEEE ICDL-Epirob in Tokyo and pre-published on arXiv, allows robots to autonomously optimize hyper-parameters tuned from any action and/or vision module, which are treated as a black box. In recent years, researchers have built robots that can complete a variety of tasks. Nonetheless, the environment in which these robots operate is often somewhat constrained. This is because in robotics, most algorithms are crafted and optimized manually by human experts to anticipate the potential challenges that the robot might encounter within a given situation.

A scientist has built a Rubik's cube that can solve itself. The toy, which has a 3D-printed robotic core, creepily completes its own iconic puzzle in just 30 seconds. Containing two internal servo motors, the entire system runs on a micro-controlling Arduino board which is small enough to fit inside the original product's dimensions. The new incarnation of the game, which first launched in 1974, is the product of Japanese creator and YouTube vlogger the'human controller'. Uploading footage of his creation, last month, the impressive scenes - which show the cube flipping and self-rotating - has already racked-up nearly 500,000 views.

This robotic Rubik's Cube is the product of a Japanese creator who's documented many of his creative projects on his YouTube channel, Human Controller. Yes, it has a custom 3D-printed core attached to servo motors that are programmed to solve the cube, which is all laid out in this process post here. But when he puts the Rubik's Cube onto the table to run free and solve itself, it really looks like a super nerdy poltergeist is doing his best to impress his seventh grade crush. As much as I want to believe this is the work of a really smart ghost, the self-solving Rubik's Cube is a project that's been years in the making. The cube, which is now the same dimensions as a standard Rubik's Cube, originally started off much bigger (seen here in the version he posted a year ago).

This paper presents a novel SHOI tableau calculus which incorporates algebraic reasoning for deciding ontology consistency. Numerical restrictions imposed by nominals, existential and universal restrictions are encoded into a set of linear inequalities. Column generation and branch-and-price algorithms are used to solve these inequalities. Our preliminary experiments indicate that this calculus performs better on SHOI ontologies than standard tableau methods.

We realized manipulation of Rubik's cube using a high-speed robot hand with three fingers. The experimental system consists of a high-speed vision and a high-speed robot hand, and the high-speed vision can calculate the center of gravity position and angle of the Rubik's cube at 500 fps. The manipulation realized in this research is a total of three operations, two kinds of regrasping and one-face turning of the Rubik's cube. By combining these three operations all the faces can be turned. In the experiment, these three operations were performed in a row in 1 second and we succeeded in 30 continuous operations in 10 seconds.

Software defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller, that can be programmed and used as the brain of the network. Recently, the research community has showed an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN. In this study, we provide a detailed overview of the recent efforts to include AI in SDN. Our study showed that the research efforts focused on three main sub-fields of AI namely: machine learning, meta-heuristics and fuzzy inference systems. Accordingly, in this work we investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm.

This paper presents a practical application of Answer Set Programming to the understanding of narratives about restaurants. While this task was investigated in depth by Erik Mueller, exceptional scenarios remained a serious challenge for his script-based story comprehension system. We present a methodology that remedies this issue by modeling characters in a restaurant episode as intentional agents. We focus especially on the refinement of certain components of this methodology in order to increase coverage and performance. We present a restaurant story corpus that we created to design and evaluate our methodology.

The Pentagon is launching a new artificial intelligence push it calls'AI Next' which aims to improve the relationship between machines and humans. As part of the multi-year initiative, the US Defense Advanced Research Projects Agency (DARPA) is set to invest more than $2bn in the programme. In promo material for the programme, DARPA says AI Next will accelerate "the Third Wave" which enables machines to adapt to changing situations. For instance, adaptive reasoning will enable computer algorithms to discern the difference between the use of'principal' and'principle' based on the analysis of surrounding words to help determine context. "Today, machines lack contextual reasoning capabilities and their training must cover every eventuality – which is not only costly – but ultimately impossible. We want to explore how machines can acquire human-like communication and reasoning capabilities, with the ability to recognise new situations and environments and adapt to them."

IBM and Galp, a Portuguese energy group with a global footprint, have developed an AI-based advisor to enhance seismic interpretation in the oil and gas exploration area. This tool can facilitate creation of enhanced geological models, risk assessment of new prospects, and optimization of the placement of new oil wells. As global energy consumption increases and much of the globe still relies on fossil fuels to supply its energy needs, the oil and gas industry is facing the challenge of finding new resources. More advanced analysis and computing are required to find and evaluate hidden sources of fuel. IBM and Galp are helping to solve that.

Knowledge representation learning aims at modeling knowledge graph by encoding entities and relations into a low dimensional space. Most of the traditional works for knowledge embedding need negative sampling to minimize a margin-based ranking loss. However, those works construct negative samples through a random mode, by which the samples are often too trivial to fit the model efficiently. In this paper, we propose a novel knowledge representation learning framework based on Generative Adversarial Networks (GAN). In this GAN-based framework, we take advantage of a generator to obtain high-quality negative samples. Meanwhile, the discriminator in GAN learns the embeddings of the entities and relations in knowledge graph. Thus, we can incorporate the proposed GAN-based framework into various traditional models to improve the ability of knowledge representation learning. Experimental results show that our proposed GAN-based framework outperforms baselines on triplets classification and link prediction tasks.