In this paper we accelerate the column generation (CG) solution to expanded linear programming (LP) relaxations (Barnhart et al. 1996) using dual optimal inequalities (Ben Amor et al. 2006) (DOI). Expanded LP relaxations are used to solve integer linear programs (ILPs) for which compact LP relaxations are very loose. In contrast to compact LP relaxations, which contain a small number of variables, expanded LP relaxations contain a massive number of variables (called columns). However an expanded LP relaxation is often much tighter than the corresponding compact LP relaxation, and permits efficient (often in practice exact) optimization (Yarkony et al. 2019) of the corresponding ILP. To solve expanded LP relaxations, CG is used. Since the set of all feasible columns is enormous and can not be easily enumerated, a sufficient set is constructed iteratively using CG. The process of identifying negative reduced cost columns is called pricing.

Self/other distinction and self-recognition are important skills for interacting with the world, as it allows humans to differentiate own actions from others and be self-aware. However, only a selected group of animals, mainly high order mammals such as humans, has passed the mirror test, a behavioural experiment proposed to assess self-recognition abilities. In this paper, we describe self-recognition as a process that is built on top of body perception unconscious mechanisms. We present an algorithm that enables a robot to perform non-appearance self-recognition on a mirror and distinguish its simple actions from other entities, by answering the following question: am I generating these sensations? The algorithm combines active inference, a theoretical model of perception and action in the brain, with neural network learning. The robot learns the relation between its actions and its body with the effect produced in the visual field and its body sensors. The prediction error generated between the models and the real observations during the interaction is used to infer the body configuration through free energy minimization and to accumulate evidence for recognizing its body. Experimental results on a humanoid robot show the reliability of the algorithm for different initial conditions, such as mirror recognition in any perspective, robot-robot distinction and human-robot differentiation.

Real-world problems such as landmine detection require multiple sources of information to reduce the uncertainty of decision-making. A novel approach to solve these problems includes distributed systems, as presented in this work based on hardware and software multi-agent systems. To achieve a high rate of landmine detection, we evaluate the performance of a trained system over the distribution of samples between training and validation sets. Additionally, a general explanation of the data set is provided, presenting the samples gathered by a cooperative multi-agent system developed for detecting improvised explosive devices. The results show that input samples affect the performance of the output decisions, and a decision-making system can be less sensitive to sensor noise with intelligent systems obtained from a diverse and suitably organised training set.

Although neural models have performed impressively well on various tasks such as image recognition and question answering, their reasoning ability has been measured in only few studies. In this work, we focus on spatial reasoning and explore the spatial understanding of neural models. First, we describe the following two spatial reasoning IQ tests: rotation and shape composition. Using well-defined rules, we constructed datasets that consist of various complexity levels. We designed a variety of experiments in terms of generalization, and evaluated six different baseline models on the newly generated datasets. We provide an analysis of the results and factors that affect the generalization abilities of models. Also, we analyze how neural models solve spatial reasoning tests with visual aids. Our findings would provide valuable insights into understanding a machine and the difference between a machine and human.

Chatbots are being used almost everywhere today from social messaging platforms to integration into websites for booking tickets, finding nearby restaurants, generating leads, buying and selling products. Some chatbots, like Ruuh by Microsoft have been able to deliver human-like conversations using artificial intelligence and deep learning. Chatbots are being used almost everywhere today from social messaging platforms to integration into websites for booking tickets, finding nearby restaurants, generating leads, buying and selling products. Some chatbots, like Ruuh by Microsoft have been able to deliver human-like conversations using artificial intelligence and deep learning. Do you remember Natasha from Hike?

Download a new guide that will help you close the machine learning skills gap in your organization. Complete the registration form to receive a complimentary PDF download of "Transform Your Workforce With Skills for Machine Learning," courtesy of Amazon Web Services. In this new manager's guide, you'll get a framework for action on closing the machine learning skills gap by developing the existing capabilities within your company. You'll learn how to assess your current workforce's skills and identify gaps; identify individuals with promise for data science roles; build data literacy across your organization; and explore diverse options for training in machine learning skills. Download the full guide now and start upskilling your workforce.

In this editorial, we take a look at a post-pandemic dystopia where AI and robotics engulf the flow of labour, talent and the economy. These are our forecasts of an economy optimised by artificial intelligence and ripened by robotics. Robots' infiltration of the workforce doesn't occur at a steady, gradual pace. Instead, automation happens in bursts, concentrated especially in bad times such as the current Covid 19-induced economic paralysis, when humans become relatively more expensive as firms' revenues rapidly decline. At these moments, employers shed less-skilled workers and replace them with technology and higher-skilled workers, which increases labor productivity as a recession tapers off.

In only a few weeks, the COVID-19 pandemic has completely disrupted our normal way of life. With many businesses shutting their doors or transitioning to a work-from-home system, adaptability to a constantly changing situation will prove key for the survival of organizations large and small. Despite everything that is going on, however, the pandemic is also spurring new innovations, particularly in the world of artificial intelligence. Here are several important ways AI is already making a difference in improving public health and safety as the world adapts to a new normal. One of the biggest challenges with this coronavirus (and the COVID-19 disease it subsequenly causes) has been how quickly it can spread.

The International Journal of Machine Consciousness, which ceased publication in 2014, is being reborn as the Journal of Artificial Intelligence and Consciousness. The aims and scope of the journal are: (i) articles that take inspiration from biological consciousness and/or that explore theoretical issues of consciousness to build robots and AI systems that show forms of functional consciousness; (ii) articles that employ robots and AI systems as tools to model and better understand biological mechanisms of consciousness; (iii) articles that discuss ethical problems emerging or uncovered through the overlap of AI and consciousness, and that investigate the ethical and societal impact of consciousness and the limits of it, and (iv) to pursue the hybridization between the field of AI and the field of consciousness studies. The journal's editor in chief is Antonio Chella, a professor of robotics at the University of Palermo, and its executive editors are David Gamez (computer science, Middlesex) and Riccardo Manzotti (philosophy, IULM Milan). There are a number of philosophers on the editorial board, including Peter Boltuc (U. The journal is currently accepting submissions and will begin publishing under its new name in 2020.

As AI is growing across the world but now it is also becoming the part of our life. It has fasten our life and make many task easy to us. Basically it is an approach to make a computer,robot or any product to think like human beings. Here are the some AI technologies to watch in 2020. Deepfake is an AI technology in which a person in one image or video is replaced with another one.