Example application domains include urban and air traffic control (Mannion et al., 2016a; Yliniemi et al., 2015), autonomous vehicles (R adulescu et al., 2018; Talpert et al., 2019) and energy systems (Walraven and Spaan, 2016; Mannion et al., 2016b; Reymond et al., 2018). Although many such problems feature multiple conflicting objectives to optimise, most MAS research focuses on agents maximising their return w.r.t. a single objective. By contrast, in multi-objective multi-agent systems (MOMAS), agents explicitly consider the possible tradeoffs between conflicting objective functions. Agents in a MOMAS receive vector-valued payoffs for their actions, where each component of a payoff vector represents the performance on a different objective. Following the utility-based approach (Roijers et al., 2013), we assume that each agent has a utility function which maps vector-valued payoffs to scalar utility values. Compromises between competing objectives are then considered on the the basis of the utility that these tradeoffs have for the users of a MOMAS. The utility-based approach naturally leads to two different optimisation criteria for agents in a MOMAS: expected scalarised returns (ESR) and scalarised expected returns (SER). To date, the differences between the SER and ESR approaches have received little attention in multi-agent settings, despite having received some attention in single-agent settings (see e.g.

Just a few days away now, Machine Learning Developers Summit, which is to be held on 22-23 Jan in Bengaluru and on 30-31 Jan in Hyderabad, has created a buzz around the tech community. With MLDS, Analytics India Magazine aims to bring in researchers and innovators together on one platform, where they will be presenting their research papers on various topics like machine learning, deep learning, and robotic process automation (RPA). About: In this paper, the author will be presenting a novel approach using graph algorithms for building a product recommendation solution for a publishing company. He will be talking about the developed approach that focuses on the popular books and courses inside a local community identified by the graph algorithms to generate recommendations. About: In this paper, the author will be presenting an idea where they use neural network architectures for attention mechanisms to spot the people who are suffering from prolonged stress.

A fundamental question for companies is: How to make good decisions with the increasing amount of logged data?. Currently, companies are doing online tests (e.g. A/B tests) before making decisions. However, online tests can be expensive because testing inferior decisions hurt users' experiences. On the other hand, offline causal inference analyzes logged data alone to make decisions, but once a wrong decision is made by the offline causal inference, this wrong decision will continuously to hurt all users' experience. In this paper, we unify offline causal inference and online bandit learning to make the right decision. Our framework is flexible to incorporate various causal inference methods (e.g. matching, weighting) and online bandit methods (e.g. UCB, LinUCB). For these novel combination of algorithms, we derive theoretical bounds on the decision maker's "regret" compared to its optimal decision. We also derive the first regret bound for forest-based online bandit algorithms. Experiments on synthetic data show that our algorithms outperform methods that use only the logged data or only the online feedbacks.

We introduce a deep neural network based method for solving a class of elliptic partial differential equations. We approximate the solution of the PDE with a deep neural network which is trained under the guidance of a probabilistic representation of the PDE in the spirit of the Feynman-Kac formula. The solution is given by an expectation of a martingale process driven by a Brownian motion. As Brownian walkers explore the domain, the deep neural network is iteratively trained using a form of reinforcement learning. Our method is a 'Derivative-Free Loss Method' since it does not require the explicit calculation of the derivatives of the neural network with respect to the input neurons in order to compute the training loss. The advantages of our method are showcased in a series of test problems: a corner singularity problem, an interface problem, and an application to a chemotaxis population model.

It is crucial for the wine industry to have methods like electronic nose systems (E-Noses) for real-time monitoring thresholds of acetic acid in wines, preventing its spoilage or determining its quality. In this paper, we prove that the portable and compact self-developed E-Nose, based on thin film semiconductor (SnO2) sensors and trained with an approach that uses deep Multilayer Perceptron (MLP) neural network, can perform early detection of wine spoilage thresholds in routine tasks of wine quality control. To obtain rapid and online detection, we propose a method of rising-window focused on raw data processing to find an early portion of the sensor signals with the best recognition performance. Our approach was compared with the conventional approach employed in E-Noses for gas recognition that involves feature extraction and selection techniques for preprocessing data, succeeded by a Support Vector Machine (SVM) classifier. The results evidence that is possible to classify three wine spoilage levels in 2.7 seconds after the gas injection point, implying in a methodology 63 times faster than the results obtained with the conventional approach in our experimental setup.

Deep learning (DL) is transforming whole industries as complicated decision-making processes are being automated by Deep Neural Networks (DNNs) trained on real-world data. Driven in part by a rapidly-expanding literature on DNN approximation theory showing that DNNs can approximate a rich variety of functions, these tools are increasingly being considered for problems in scientific computing. Yet, unlike more traditional algorithms in this field, relatively little is known about DNNs from the principles of numerical analysis, namely, stability, accuracy, computational efficiency and sample complexity. In this paper we introduce a computational framework for examining DNNs in practice, and use it to study their empirical performance with regard to these issues. We examine the performance of DNNs of different widths and depths on a variety of test functions in various dimensions, including smooth and piecewise smooth functions. We also compare DL against best-in-class methods for smooth function approximation based on compressed sensing. Our main conclusion is that there is a crucial gap between the approximation theory of DNNs and their practical performance, with trained DNNs performing relatively poorly on functions for which there are strong approximation results (e.g. smooth functions), yet performing well in comparison to best-in-class methods for other functions. Finally, we present a novel practical existence theorem, which asserts the existence of a DNN architecture and training procedure which offers the same performance as current best-in-class schemes. This result indicates the potential for practical DNN approximation, and the need for future research into practical architecture design and training strategies.

Accurate and reliable forecasting of total cloud cover (TCC) is vital for many areas such as astronomy, energy demand and production, or agriculture. Most meteorological centres issue ensemble forecasts of TCC, however, these forecasts are often uncalibrated and exhibit worse forecast skill than ensemble forecasts of other weather variables. Hence, some form of post-processing is strongly required to improve predictive performance. As TCC observations are usually reported on a discrete scale taking just nine different values called oktas, statistical calibration of TCC ensemble forecasts can be considered a classification problem with outputs given by the probabilities of the oktas. This is a classical area where machine learning methods are applied. We investigate the performance of post-processing using multilayer percep-tron (MLP) neural networks, gradient boosting machines (GBM) and random forest (RF) methods. Based on the European Centre for Medium-Range Weather Forecasts global TCC ensemble forecasts for 2002-2014 we compare these approaches with the proportional odds logistic regression (POLR) and multiclass logistic regression (MLR) models, as well as the raw TCC ensemble forecasts. We further assess whether improvements in forecast skill can be obtained by incorporating ensemble forecasts of precipitation as additional predictor. Compared to the raw ensemble, all calibration methods result in a significant improvement in forecast skill. RF models provide the smallest increase in predictive performance, while MLP, POLR and GBM approaches perform best. Key words: ensemble calibration; gradient boosting machine; logistic regression; mul-tilayer perceptron; random forest; total cloud cover 1 Introduction Reliable and accurate prediction of total cloud cover (TCC) has a principal importance in observational astronomy (Ye and Chen, 2013) and in the prediction of photovoltaic energy production, as it is the main cause of variation in solar-radiation energy supply (Matuszko, 2012; McEvoy et al., 2012), but it is also of great relevance in agriculture, tourism and in some other fields of economy.

Carmaker Toyota has unveiled plans for a 2,000-person "city of the future," where it will test autonomous vehicles, smart technology and robot-assisted living. The ambitious project, dubbed Woven City, is set to break ground next year in the foothills of Japan's Mount Fuji, about 60 miles from Tokyo. Announcing the project at the Consumer Electronics Show (CES) in Las Vegas, Toyota's CEO Akio Toyoda described the new city as a "living laboratory" that will allow researchers, scientists and engineers to test emerging technology in a "real-life environment." A digital mock-up shows small autonomous vehicles operating alongside pedestrians. "With people buildings and vehicles all connected and communicating with each other through data and sensors, we will be able to test AI technology, in both the virtual and the physical world, maximizing its potential," he said on stage during Tuesday's unveiling.

As disruptive technologies such as artificial intelligence (AI) fundamentally alter the way we live and do business, C-suite attitudes toward IT spending and utilization are shifting. Once considered a cost of doing business, technology is now viewed as a business driver that's critical to an organization's ability to perform core functions, even in industries far removed from Silicon Valley. However, many executives still struggle to determine the ROI to justify investments in AI and machine learning, even as AI becomes increasingly crucial to 21st century business decision-making. Except for the IT industry itself, C-suites have historically viewed IT expenses as a cost of entry to do business in the digital age, not revenue-generating investments. Then came new technologies such as mobile, cloud computing and the internet of things (IoT).

"Time series models are used to forecast future events based on previous events that have been observed (and data collected) at regular time intervals." We will be taking a small forecasting problem and try to solve it till the end learning time series forecasting alongside. Time series forecasting is a technique for the prediction of events through a sequence of time. The technique is used across many fields of study, from geology to behavior to economics. The techniques predict future events by analyzing the trends of the past, on the assumption that future trends will hold similar to historical trends. Time series forecasting is sometimes just the analysis of experts studying a field and offering their predictions.