Multi-agent planning problems with constraints on global resource consumption occur in several domains. Existing algorithms for solving Multi-agent Markov Decision Processes can compute policies that meet a resource constraint in expectation, but these policies provide no guarantees on the probability that a resource constraint violation will occur. We derive a method to bound constraint violation probabilities using Hoeffding's inequality. This method is applied to two existing approaches for computing policies satisfying constraints: the Constrained MDP framework and a Column Generation approach. We also introduce an algorithm to adaptively relax the bound up to a given maximum violation tolerance. Experiments on a hard toy problem show that the resulting policies outperform static optimal resource allocations to an arbitrary level. By testing the algorithms on more realistic planning domains from the literature, we demonstrate that the adaptive bound is able to efficiently trade off violation probability with expected value, outperforming state-of-the-art planners.

Bäckström has previously studied a number of optimization problems for partial-order plans, like finding a minimum deordering (MCD) or reordering (MCR), and finding the minimum parallel execution length (PPL), which are all NP-complete. We revisit these problems, but applying parameterized complexity analysis rather than standard complexity analysis. We consider various parameters, including both the original and desired size of the plan order, as well as its width and height. Our findings include that MCD and MCR are W[2]-hard and in W[P] when parameterized with the desired order size, and MCD is fixed-parameter tractable (fpt) when parameterized with the original order size. Problem PPL is fpt if parameterized with the size of the non-concurrency relation, but para-NP-hard in most other cases. We also consider this problem when the number (k) of agents, or processors, is restricted, finding that this number is a crucial parameter; this problem is fixed-parameter tractable with the order size, the parallel execution length and k as parameter, but para-NP-hard without k as parameter.

Structured regression on graphs aims to predict response variables from multiple nodes by discovering and exploiting the dependency structure among response variables. This problem is challenging since dependencies among response variables are always unknown, and the associated prior knowledge is non-symmetric. In previous studies, various promising solutions were proposed to improve structured regression by utilizing symmetric prior knowledge, learning sparse dependency structure among response variables, or learning representations of attributes of multiple nodes. However, none of them are capable of efficiently learning dependency structure while incorporating non-symmetric prior knowledge. To achieve these objectives, we proposed Continuous Conditional Dependency Network (CCDN) for structured regression. The intuitive idea behind this model is that each response variable is not only dependent on attributes from the same node, but also on response variables from all other nodes. This results in a joint modeling of local conditional probabilities. The parameter learning is formulated as a convex optimization problem and an effective sampling algorithm is proposed for inference. CCDN is flexible in absorbing non-symmetric prior knowledge. The performance of CCDN on multiple datasets provides evidence of its structure recovery ability and superior effectiveness and efficiency as compared to the state-of-the-art alternatives.

How AI-based virtual replicas can help financial institutions better predict customer behavior. Ever since wind farms became part of the US energy market in the 1980s, operators have worked hard to compete with more established energy providers. And they've found that when you're trying to improve operational efficiency, manually inspecting broken parts on a wind turbine just won't cut it. Instead, wind farm owners use artificial intelligence (AI) to find ways to produce more energy with less wear and tear on their equipment. Meet digital twins: models that are virtual replicas, or "twins," of each physical asset (the wind turbine), the underlying parts (the rotors), and the entire system (the fleet of wind farms).

The coastal prefecture of Fukushima has faced a difficult road since a devastating earthquake and tsunami rocked the area in 2011, killing tens of thousands and causing its nuclear reactor to melt down, leaking radiation and rendering the surrounding provinces uninhabitable. Since then, the plant's operator, Tokyo Electric Power Company (TEPCO), has been working to clean and decommission the facility. In January, the company bumped its estimate for a full cleaning to $188 billion, noting it would probably take decades. Radiation levels from the reactors should have faded over time, but TEPCO said Thursday levels inside the Fukushima Daiichi plant reached such astronomical levels, not even a cleaning robot could survive inside. The previous radiation high, measured one year after the disaster, was 73 Sieverts per hour.

Nasa has narrowed down its quest to find alien life on the red planet by pin-pointing three potential target sites for its Mars 2020 mission. The US space agency will send its Mars 2020 rover to one of three drilling sites - each selected for their potential to host extraterrestrial life. The automated robot rover will scan the surface of the chosen landing site before taking detailed pictures and collecting rocky samples to bring back to Earth. The American space agency picked the three potential drilling sites during a workshop with planetary scientists in California on February 10. The site with the most votes - the Jezero crater - was once home to an ancient Martian lake.

Can computers solve all our problems? Well, when combined with the creative power of humans, the answer is… maybe. Every day, we get closer to solving some of the most complex and serious problems facing humanity through use of new technologies including big data and artificial intelligence (AI). Modelling climate changes on a global scale remains a very complex and complicated proposition, but AI and big data are making it a bit easier for scientists to understand and predict the effects of climate change. AI might also help scientists and lawmakers make the best decisions based on the best information today.

You've probably comes to mind the question, whether the Artificial Intelligence (AI) is important?;

The virtual world is built on physical infrastructure. Every search that gets submitted, email sent, page served, comment posted, and video loaded passes through data centers that can be larger than a football field. Those thousands of racks of humming servers use vast amounts of energy; together, all existing data centers use roughly 2% of the world's electricity, and if left unchecked, this energy demand could grow as rapidly as Internet use. So making data centers run as efficiently as possible is a very big deal. Thankfully, despite skyrocketing demand for computing, data center electricity use has flattened over the past few years, largely due to enormous opportunities to improve efficiency as these facilities scale up.1 But capturing these opportunities can be a very complicated process.

The inference of correlated signal fields with unknown correlation structures is of high scientific and technological relevance, but poses significant conceptual and numerical challenges. To address these, we develop the correlated signal inference (CSI) algorithm within information field theory (IFT) and discuss its numerical implementation. To this end, we introduce the free energy exploration (FrEE) strategy for numerical information field theory (NIFTy) applications. The FrEE strategy is to let the mathematical structure of the inference problem determine the dynamics of the numerical solver. FrEE uses the Gibbs free energy formalism for all involved unknown fields and correlation structures without marginalization of nuisance quantities. It thereby avoids the complexity marginalization often impose to IFT equations. FrEE simultaneously solves for the mean and the uncertainties of signal, nuisance, and auxiliary fields, while exploiting any analytically calculable quantity. Finally, FrEE uses a problem specific and self-tuning exploration strategy to swiftly identify the optimal field estimates as well as their uncertainty maps. For all estimated fields, properly weighted posterior samples drawn from their exact, fully non-Gaussian distributions can be generated. Here, we develop the FrEE strategies for the CSI of a normal, a log-normal, and a Poisson log-normal IFT signal inference problem and demonstrate their performances via their NIFTy implementations.