Manufacturing companies face challenges when it comes to quickly adapting their production control to fluctuating demands or changing requirements. Control approaches that encapsulate production functions as services have shown to be promising in order to increase the flexibility of Cyber-Physical Production Systems. But an existing challenge of such approaches is finding a production plan based on provided functionalities for a demanded product, especially when there is no direct (i.e., syntactic) match between demanded and provided functions. While there is a variety of approaches to production planning, flexible production poses specific requirements that are not covered by existing research. In this contribution, we first capture these requirements for flexible production environments. Afterwards, an overview of current Artificial Intelligence approaches that can be utilized in order to overcome the aforementioned challenges is given. For this purpose, we focus on planning algorithms, but also consider models of production systems that can act as inputs to these algorithms. Approaches from both symbolic AI planning as well as approaches based on Machine Learning are discussed and eventually compared against the requirements. Based on this comparison, a research agenda is derived.

Intelligent decision support (IDS) systems leverage artificial intelligence techniques to generate recommendations that guide human users through the decision making phases of a task. However, a key challenge is that IDS systems are not perfect, and in complex real-world scenarios may produce incorrect output or fail to work altogether. The field of explainable AI planning (XAIP) has sought to develop techniques that make the decision making of sequential decision making AI systems more explainable to end-users. Critically, prior work in applying XAIP techniques to IDS systems has assumed that the plan being proposed by the planner is always optimal, and therefore the action or plan being recommended as decision support to the user is always correct. In this work, we examine novice user interactions with a non-robust IDS system -- one that occasionally recommends the wrong action, and one that may become unavailable after users have become accustomed to its guidance. We introduce a novel explanation type, subgoal-based explanations, for planning-based IDS systems, that supplements traditional IDS output with information about the subgoal toward which the recommended action would contribute. We demonstrate that subgoal-based explanations lead to improved user task performance, improve user ability to distinguish optimal and suboptimal IDS recommendations, are preferred by users, and enable more robust user performance in the case of IDS failure

Google CEO Sundar Pichai has said that AI and automation are'more profound than the discovery of electricity or fire." The benefits of automating mundane, repetitive admin tasks are clear: increasing and diversifying revenue, boosting employee productivity, and optimising legacy technology are just a handful of tasks that businesses stand to benefit from. In recent times, global organizations have leaned on Robotic Process Automation (RPA) to deliver this automation, using hordes of software robots to replace actions reliant on human inputs at a lower cost. As a result, the global RPA market is expected to reach USD 7.64 billion by 2028. However, there is a disconnect between the end-to-end'automation dream' companies that have been sold, and many of the offerings in the world of RPA are failing to deliver on promises made. Legacy RPA tools have been great at automating simple, siloed tasks for a number of years, yet fall down when asked to do this intelligently, and at scale.

To this day, a variety of approaches for providing local interpretability of blackbox machine learning models have been introduced. Unfortunately, all of these methods suffer from one or more of the following deficiencies: They are either difficult to understand themselves, they work on a per-feature basis and ignore the dependencies between features and/or they only focus on those features asserting the decision made by the model. To address these points, this work introduces a reinforcement learning-based approach called Monte Carlo tree search for eXplainable Artificial Intelligent (McXai) to explain the decisions of any black-box classification model (classifier). In one game, the reward is maximized by finding feature sets that support the decision of the classifier, while in the second game, finding feature sets leading to alternative decisions maximizes the reward. The result is a human friendly representation as a tree structure, in which each node represents a set of features to be studied with smaller explanations at the top of the tree. Our experiments show, that the features found by our method are more informative with respect to classifications than those found by classical approaches like LIME and SHAP. Furthermore, by also identifying misleading features, our approach is able to guide towards improved robustness of the black-box model in many situations. With the successful application of machine learning-based classification in a growing number of domains, there is an increasingly high demand for understanding the predictive decisions of machine learning models. One concrete motivation for this is the proliferation of machine learning in the natural sciences, where interpretability is a prerequisite to ensure the scientific value of the results.

Researchers from security firm Sansec recently discovered a new Linux remote access trojan (RAT), tracked as CronRAT, that hides in the Linux task scheduling system (cron) on February 31st. CronRAT is employed in Magecart attacks against online stores web stores and enables attackers to steal credit card data by deploying online payment skimmers on Linux servers. While investigating CronRAT infections in North America and Europe the researchers spotted a new malware, dubbed NginRAT, that hides on Nginx servers bypassing security solutions. Like CronRAT, also NginRAT works as a "server-side Magecart," it injects itself into an Nginx process. Experts pointed out that a rogue Nginx process could not be distinguished from the original.

Autonomous vehicles use 3D sensors for perception. Cooperative perception enables vehicles to share sensor readings with each other to improve safety. Prior work in cooperative perception scales poorly even with infrastructure support. AutoCast enables scalable infrastructure-less cooperative perception using direct vehicle-to-vehicle communication. It carefully determines which objects to share based on positional relationships between traffic participants, and the time evolution of their trajectories. It coordinates vehicles and optimally schedules transmissions in a distributed fashion. Extensive evaluation results under different scenarios show that, unlike competing approaches, AutoCast can avoid crashes and near-misses which occur frequently without cooperative perception, its performance scales gracefully in dense traffic scenarios providing 2-4x visibility into safety critical objects compared to existing cooperative perception schemes, its transmission schedules can be completed on the real radio testbed, and its scheduling algorithm is near-optimal with negligible computation overhead.

Urban planning refers to the efforts of designing land-use configurations given a region. However, to obtain effective urban plans, urban experts have to spend much time and effort analyzing sophisticated planning constraints based on domain knowledge and personal experiences. To alleviate the heavy burden of them and produce consistent urban plans, we want to ask that can AI accelerate the urban planning process, so that human planners only adjust generated configurations for specific needs? The recent advance of deep generative models provides a possible answer, which inspires us to automate urban planning from an adversarial learning perspective. However, three major challenges arise: 1) how to define a quantitative land-use configuration? 2) how to automate configuration planning? 3) how to evaluate the quality of a generated configuration? In this paper, we systematically address the three challenges. Specifically, 1) We define a land-use configuration as a longitude-latitude-channel tensor. 2) We formulate the automated urban planning problem into a task of deep generative learning. The objective is to generate a configuration tensor given the surrounding contexts of a target region. 3) We provide quantitative evaluation metrics and conduct extensive experiments to demonstrate the effectiveness of our framework.

Ultimate Goal Setting and Achieving is the course for you if you have ever looked at someone else and thought "I want to be like them!" Sometimes when you try to be like that person, you might feel upset or even angry because despite all of your hard work it seems like you are getting nowhere, and everyone else is doing better than you. That's because hard work alone is useless. You need direction and purpose. Think of a car race. It doesn't matter how fast the car is.

A record number of NFL players tested positive for COVID-19 last week, leading to postponed games. LAS VEGAS, NEVADA – Football fans got double the action Monday night and will again Tuesday night. That's because the NFL rearranged its Week 15 schedule as COVID-19 cases surge among the players, 100 of whom tested positive over three days last week. For the second year in a row, professional sports teams aren't just worried about their opponent -- they're worried about whether there are enough players to play the game. "Just like anywhere else, you have to think about can you operate your business if everybody is sick?" said Brian Labus, infectious disease epidemiologist and assistant professor at the University of Nevada-Las Vegas.

To effectively use an abstract (PDDL) planning domain to achieve goals in an unknown environment, an agent must instantiate such a domain with the objects of the environment and their properties. If the agent has an egocentric and partial view of the environment, it needs to act, sense, and abstract the perceived data in the planning domain. Furthermore, the agent needs to compile the plans computed by a symbolic planner into low level actions executable by its actuators. This paper proposes a framework that aims to accomplish the aforementioned perspective and allows an agent to perform different tasks. For this purpose, we integrate machine learning models to abstract the sensory data, symbolic planning for goal achievement and path planning for navigation. We evaluate the proposed method in accurate simulated environments, where the sensors are RGB-D on-board camera, GPS and compass.