There are several motivations for Shell to revolutionise their company via the use of AI and data. The oil and gas business is at a crossroads because to rising energy needs, disconnected environments, and pressure to combat climate change. Shell and other energy firms have a choice between maintaining the current quo and embracing a low-carbon energy future. End-to-end processes must be optimised and kept up at scale as we move toward a more dispersed, diversified, and decentralised energy system. Therefore, it is essential to find solutions that can be quickly and universally implemented.

Working at Shell's Deepwater division in New Orleans gives Barbara Waelde a front-row seat to how the right data can unlock crucial information for the oil giant. So when her supervisor asked her last year if she was interested in a program that could sharpen her digital and data science capabilities, Waelde, 55, jumped at the chance. Since she began her online coursework, the seven-year Shell veteran has learned Python programming, supervised learning algorithms and data modeling, among other skills. Shell began making these online courses available to U.S. employees long before COVID-19 upended daily life. And according to the oil giant, there are no plans to halt or cancel any of them, despite the fact that on March 23 it announced plans to slash operating costs by $9 billion.

Shell has a broader strategy to embed AI across its operations, a move that has helped the oil giant lower costs and avoid downtime. Other oil-and-gas companies that have tapped AI to improve operations and reduce costs include Exxon Mobil Corp., BP PLC and Chevron Corp. "Artificial intelligence enables us to process the vast quantity of data across our businesses to generate new insights which can keep us ahead of the competition," said Yuri Sebregts, Shell's chief technology officer, in an email. The initiative at Shell expands a 2019 yearlong pilot program with Udacity, based in Mountain View, Calif., that included about 250 Shell data scientists and software engineers. They picked up AI skills such as reinforcement learning, a type of machine learning where algorithms learn the correct way to perform an action based on trial-and-error and observations. Shell employees could use AI expertise, for example, to better predict equipment failures and automatically identify areas within a facility to reduce carbon emissions, said Dan Jeavons, Shell's general manager of data science.

Royal Dutch Shell is heavily investing in research and development of artificial intelligence (AI), which it hopes will provide solutions to some of its most pressing challenges. From meeting the demands of a transitioning energy market, urgently in need of cleaner and more efficient power, to improving safety on the forecourts of its service stations, AI is at the top of the agenda. I have been working with Shell over the past months to help create a data strategy, which gave me a thorough insight into Shell's AI priorities and initiatives. Current initiatives include deploying reinforcement learning in its exploration and drilling program, to reduce the cost of extracting the gas that still drives a significant proportion of its revenues. Elsewhere across its global business, Shell is rolling out AI at its public electric car charging stations, to manage the shifting demand for power throughout a day.

Royal Dutch Shell is heavily investing in research and development of artificial intelligence (AI), which it hopes will provide solutions to some of its most pressing challenges. From meeting the demands of a transitioning energy market, urgently in need of cleaner and more efficient power, to improving safety on the forecourts of its service stations, AI is at the top of the agenda. I have been working with Shell over the past months to help create a data strategy, which gave me a thorough insight into Shell's AI priorities and initiatives. Current initiatives include deploying reinforcement learning in its exploration and drilling program, to reduce the cost of extracting the gas that still drives a significant proportion of its revenues. Elsewhere across its global business, Shell is rolling out AI at its public electric car charging stations, to manage the shifting demand for power throughout a day.

Royal Dutch Shell is heavily investing in research and development of artificial intelligence (AI), which it hopes will provide solutions to some of its most pressing challenges. From meeting the demands of a transitioning energy market, urgently in need of cleaner and more efficient power, to improving safety on the forecourts of its service stations, AI is at the top of the agenda. I have been working with Shell over the past months to help create a data strategy, which gave me a thorough insight into Shell's AI priorities and initiatives. Current initiatives include deploying reinforcement learning in its exploration and drilling program, to reduce the cost of extracting the gas that still drives a significant proportion of its revenues. Elsewhere across its global business, Shell is rolling out AI at its public electric car charging stations, to manage the shifting demand for power throughout a day.

The binary Constraint Satisfaction Problem (CSP) is to decide whether there exists an assignment to a set of variables which satisfies specified constraints between pairs of variables. A CSP instance can be presented as a labelled graph (called the microstructure) encoding both the forms of the constraints and where they are imposed. We consider subproblems defined by restricting the allowed form of the microstructure. One form of restriction that has previously been considered is to forbid certain specified substructures (patterns). This captures some tractable classes of the CSP, but does not capture the well-known property of acyclicity. In this paper we introduce the notion of a topological minor of a binary CSP instance. By forbidding certain patterns as topological minors we obtain a compact mechanism for expressing several novel tractable classes, including new generalisations of the class of acyclic instances.

Tractable classes constitute an important issue in Artificial Intelligence to define new islands of tractability for reasoning or problem solving. In the area of constraint networks, numerous tractable classes have been defined, and recently, the Broken Triangle Property (BTP) has been shown as one of the most important of them, this class including several classes previously defined. In this paper, we propose a new class called ETP for Extendable-Triple Property, which generalizes BTP, by including it. Combined with the verification of the Strong-Path-Consistency, ETP is shown to be a new tractable class. Moreover, this class inherits some desirable properties of BTP including the fact that the instances of this class can be solved thanks to usual algorithms (such as MAC or RFL) used in most solvers. We give the theoretical material about this new class and we present an experimental study which shows that from a practical viewpoint, it seems more usable in practice than BTP.

Constraint programming is a natural paradigm for many combinatorial optimisation problems. The complexity of constraint satisfaction for various forms of constraints has been widely-studied, both to inform the choice of appropriate algorithms, and to understand better the boundary between polynomial-time complexity and NP-hardness. In constraint programming it is well-known that any constraint satisfaction problem can be converted to an equivalent binary problem using the so-called dual encoding. Using this standard approach any fixed collection of constraints, of arbitrary arity, can be converted to an equivalent set of constraints of arity at most two. Here we show that this transformation, although it changes the domain of the constraints, preserves all the relevant algebraic properties that determine the complexity. Moreover, we show that the dual encoding preserves many of the key algorithmic properties of the original instance. We also show that this remains true for more general valued constraint languages, where constraints may assign different cost values to different assignments. Hence, we obtain a simple proof of the fact that to classify the computational complexity of all valued constraint languages it suffices to classify only binary valued constraint languages.

In this paper, we investigate the hybrid tractability of binary Quantified Constraint Satisfaction Problems (QCSPs). First, a basic tractable class of binary QCSPs is identified by using the broken-triangle property. In this class, the variable ordering for the broken-triangle property must be same as that in the prefix of the QCSP. Second, we break this restriction to allow that existentially quantified variables can be shifted within or out of their blocks, and thus identify some novel tractable classes by introducing the broken-angle property. Finally, we identify a more generalized tractable class, i.e., the min-of-max extendable class for QCSPs.