This paper presents a novel data-driven approach to vehicle motion planning and control in off-road driving scenarios. For autonomous off-road driving, environmental conditions impact terrain traversability as a function of weather, surface composition, and slope. Geographical information system (GIS) and National Centers for Environmental Information datasets are processed to provide this information for interactive planning and control system elements. A top-level global route planner (GRP) defines optimal waypoints using dynamic programming (DP). A local path planner (LPP) computes a desired trajectory between waypoints such that infeasible control states and collisions with obstacles are avoided. The LPP also updates the GRP with real-time sensing and control data. A low-level feedback controller applies feedback linearization to asymptotically track the specified LPP trajectory. Autonomous driving simulation results are presented for traversal of terrains in Oregon and Indiana case studies.

We present an extension of Monte Carlo Tree Search (MCTS) that strongly increases its efficiency for trees with asymmetry and/or loops. Asymmetric termination of search trees introduces a type of uncertainty for which the standard upper confidence bound (UCB) formula does not account. Our first algorithm (MCTS-T), which assumes a non-stochastic environment, backs-up tree structure uncertainty and leverages it for exploration in a modified UCB formula. Results show vastly improved efficiency in a well-known asymmetric domain in which MCTS performs arbitrarily bad. Next, we connect the ideas about asymmetric termination to the presence of loops in the tree, where the same state appears multiple times in a single trace. An extension to our algorithm (MCTS-T+), which in addition to non-stochasticity assumes full state observability, further increases search efficiency for domains with loops as well. Benchmark testing on a set of OpenAI Gym and Atari 2600 games indicates that our algorithms always perform better than or at least equivalent to standard MCTS, and could be first-choice tree search algorithms for non-stochastic, fully-observable environments.

MAOS-FSP [1] is a multiagent optimization system (MAOS) for solving the Flowshop Scheduling Problem (FSP). MAOS-FSP shares the MAOS kernel with other MAOS applications (e.g. MAOS-GCP and MAOS-TSP), and contains some modules that are specifically for tacking FSP. Related Information: Please find other related code and software in our Source Code Library. License information: MAOS-FSP is free software; you can redistribute it and/or modify it under the Creative Commons Non-Commercial License 3.0.

As industries journey towards the Fourth Industrial Revolution, where people and robots will work together seamlessly, it's becoming more important than ever for organisations to integrate artificial intelligence (AI), robotics and the internet of things (IoT) into their digital game plan. However, much like policy makers, business leaders are struggling to keep up with these advancements. They're failing to leverage technology in areas such as human resources, payroll and workforce management. According to Deloitte's Human Capital Trends Report 2018, almost 95% of organisations worldwide don't know how to manage their workforce effectively. It's a confronting statistic and it's one Jarrod McGrath, founder and CEO of strategic workforce management consultancy Smart WFM, has set out to change. "I couldn't believe how high this figure was," says Jarrod.

A modern radar may be designed to perform multiple functions, such as surveillance, tracking, and fire control. Each function requires the radar to execute a number of transmit-receive tasks. A radar resource management (RRM) module makes decisions on parameter selection, prioritization, and scheduling of such tasks. RRM becomes especially challenging in overload situations, where some tasks may need to be delayed or even dropped. In general, task scheduling is an NP-hard problem. In this work, we develop the branch-and-bound (B&B) method which obtains the optimal solution but at exponential computational complexity. On the other hand, heuristic methods have low complexity but provide relatively poor performance. We resort to machine learning-based techniques to address this issue; specifically we propose an approximate algorithm based on the Monte Carlo tree search method. Along with using bound and dominance rules to eliminate nodes from the search tree, we use a policy network to help to reduce the width of the search. Such a network can be trained using solutions obtained by running the B&B method offline on problems with feasible complexity. We show that the proposed method provides near-optimal performance, but with computational complexity orders of magnitude smaller than the B&B algorithm.

While Artificial Intelligence and automation technologies is still nascent, the building blocks exist to suggest that Machine Learning could ease the burden of complex analysis, surface insights and trigger actions on behalf of managers. Workforce management is essentially the art and science of managing people in order to have a productive workforce. While there is a lot of science and methodology around this domain, with many current modern methods evolving from the foundational scientific management techniques propounded by Taylor over a century ago, it still requires the fine art of understanding an individual's capabilities and balancing human expectations to get the most out of people. Traditionally, in high empathy countries like India, this has largely been the responsibility of the manager, who balances an organisation's needs with individual wants and abilities. In short, the manager decides who needs to do what, when and where.

Hybrid manufacturing (HM) technologies combine additive and subtractive manufacturing (AM/SM) capabilities, leveraging AM's strengths in fabricating complex geometries and SM's precision and quality to produce finished parts. We present a systematic approach to automated computer-aided process planning (CAPP) for HM that can identify nontrivial, qualitatively distinct, and cost-optimal combinations of AM/SM modalities. A multimodal HM process plan is represented by a finite Boolean expression of AM and SM manufacturing primitives, such that the expression evaluates to an'as-manufactured' artifact. We show that primitives that respect spatial constraints such as accessibility and collision avoidance may be constructed by solving inverse configuration space problems on the'as-designed' artifact and manufacturing instruments. The primitives generate a finite Boolean algebra (FBA) that enumerates the entire search space for planning. The FBA's canonical intersection terms (i.e., 'atoms') provide the complete domain decomposition to reframe manufacturability analysis and process planning into purely symbolic reasoning, once a subcollection of atoms is found to be interchangeable with the design target. We demonstrate the practical potency of our framework and its computational efficiency when applied to process planning of complex 3D parts with dramatically different AM and SM instruments. Keywords: 1. Introduction Hybrid Manufacturing, Process Planning, Spatial Reasoning, Additive Manufacturing, Machining Hybrid manufacturing (HM), combining the capabilities of additive and subtractive manufacturing, is the new frontier of part fabrication. While additive manufacturing (AM) continues to enable unprecedented levels of structural complexity and customization, subtractive manufacturing (SM) remains indispensable for producing highprecision, mission-critical, and reliable mechanical components with functional interfaces. Versatile'multitasking' machines with simultaneous high-axis computer numerical control (CNC) of multiple AM and SM instruments (e.g., deposition heads and cutting tools) keep emerging on the market, enabling efficient use-cases for fabrication and repair (reviewed in Section 1.1).

We consider the problem of robot path planning in an initially unknown environment where the robot does not have access to an a priori map of its environment but is aware of some common obstacle patterns along with the paths that enable it to circumnavigate around these obstacles. In order to autonomously improve its navigation performance, the robot should be able to identify significant obstacle patterns and learn corresponding obstacle avoidance maneuvers as it navigates through different environments in order to solve its tasks. To achieve this objective, we propose a novel online algorithm called Incremental State Discovery Via Clustering (ISDC) which enables a robot to dynamically determine important obstacle patterns in its environments and their best representations as combinations of initially available basic obstacle patterns. Our results show that ISDC, when combined with our previously proposed navigation technique, was able to identify significant obstacle patterns in different environments in a time effective manner which accelerated the overall path planning and navigation times for the robots.

While Artificial Intelligence and automation technologies is still nascent, the building blocks exist to suggest that Machine Learning could ease the burden of complex analysis, surface insights and trigger actions on behalf of managers. Workforce management is essentially the art and science of managing people in order to have a productive workforce. While there is a lot of science and methodology around this domain, with many current modern methods evolving from the foundational scientific management techniques propounded by Taylor over a century ago, it still requires the fine art of understanding an individual's capabilities and balancing human expectations to get the most out of people. Traditionally, in high empathy countries like India, this has largely been the responsibility of the manager, who balances an organisation's needs with individual wants and abilities. In short, the manager decides who needs to do what, when and where.

Hospitals have to solve a thousand logistical challenges every day, but perhaps none are more difficult than operating room schedules. Surgeries can be difficult to predict -- in fact, less than half of surgeries in the U.S. start and end on time. That can create chaos for patients and doctors, and costs hospitals $5.2 billion every year, according to University of Washington spinout Perimatics. The startup, which develops a variety of technologies for hospitals, is taking aim at the operating room problem with a new AI technology that uses data on patients and surgeons to more accurately predict how long each surgery will take. The startup recently deployed the technology at a large academic medical institution in Seattle.