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.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. The latest version of the self-solving Rubik's Cube is adorable in how it tries to throw itself off of the table it's solving itself on: Not exactly an optimised solve, but we'll forgive it, because that just means we get to watch it for longer. And here's what it looks like if you're holding it: When you think of robotics, you likely think of something rigid, heavy, and built for a specific purpose.

For those outside the fold, the Rubik's cube is cognitive kryptonite. Until this week, I'd certainly never solved one. Even now, saying that I solved a Rubik's cube feels like a grievous overstatement of my accomplishments. The truth is that we--a patient pre-teen "cuber" whose solve time is 47 seconds, her slightly-less-patient middle school teacher (whose solve time, she's embarrassed to admit, is closer to a minute and a half), and me--completed a cube together. The site of my public humiliation could not have been more incongruous with the task at hand.

Aging population ratios are rising significantly. Meanwhile, smart home based health monitoring services are evolving rapidly to become a viable alternative to traditional healthcare solutions. Such services can augment qualitative analyses done by gerontologists with quantitative data. Hence, the recognition of Activities of Daily Living (ADL) has become an active domain of research in recent times. For a system to perform human activity recognition in a real-world environment, multiple requirements exist, such as scalability, robustness, ability to deal with uncertainty (e.g., missing sensor data), to operate with multi-occupants and to take into account their privacy and security. This paper attempts to address the requirements of scalability and robustness, by describing a reasoning mechanism based on modular spatial and/or temporal context models as a network of ontologies. The reasoning mechanism has been implemented in a smart home system referred to as Arianna+. The paper presents and discusses a use case, and experiments are performed on a simulated dataset, to showcase Arianna+'s modularity feature, internal working, and computational performance. Results indicate scalability and robustness for human activity recognition processes.

While the machine learning literature dedicated to fully automated reasoning algorithms is abundant, the number of methods enabling the inference process on the basis of previously defined knowledge structures is scanter. Fuzzy Cognitive Maps (FCMs) are neural networks that can be exploited towards this goal because of their flexibility to handle external knowledge. However, FCMs suffer from a number of issues that range from the limited prediction horizon to the absence of theoretically sound learning algorithms able to produce accurate predictions. In this paper, we propose a neural network system named Short-term Cognitive Networks that tackle some of these limitations. In our model weights are not constricted and may have a causal nature or not. As a second contribution, we present a nonsynaptic learning algorithm to improve the network performance without modifying the previously defined weights. Moreover, we derive a stop condition to prevent the learning algorithm from iterating without decreasing the simulation error.