It's rare for a turn-based RPG to excite the editorial team beyond Engadget's usual core RPG fans. Clair Obscur: Expedition 33 has many excited, however. Maybe it's the characters, bolstered by a starry voice artist roster, and nuanced animation and story. Expedition 33 does a great job setting up its world in a way that allows everyone to get on board. Lumiere, which seems to be a chunk of Paris plus change, is doomed to repeat a cycle of death, with a powerful godlike figure, the Paintress, looming on the horizon.

Running late to a Microsoft Teams meeting can land you seriously out of sync with your colleagues and the latest updates from work. A coworker is resigning, you're about to get a promotion, the company is being liquidated -- these are just some of the real-life bombshells that could have been dropped while you were waiting for your morning coffee order to arrive. You could spend the whole rest of the meeting interrupting your colleagues to find out what juicy tidbits you missed, but doing that isn't going to win you any fans. A better option is to just use Copilot to recap what happened in your absence. For Copilot recap to work in Teams you'll need to have a current license for Microsoft 365 and Copilot.

Multi-Robot Task Allocation (MRTA) is a problem that arises in many application domains including package delivery, warehouse robotics, and healthcare. In this work, we consider the problem of MRTA for a dynamic stream of tasks with task deadlines and capacitated agents (capacity for more than one simultaneous task). Previous work commonly focuses on the static case, uses specialized algorithms for restrictive task specifications, or lacks guarantees. We propose an approach to Dynamic MRTA for capacitated robots that is based on Satisfiability Modulo Theories (SMT) solving and addresses these concerns. We show our approach is both sound and complete, and that the SMT encoding is general, enabling extension to a broader class of task specifications. We show how to leverage the incremental solving capabilities of SMT solvers, keeping learned information when allocating new tasks arriving online, and to solve non-incrementally, which we provide runtime comparisons of. Additionally, we provide an algorithm to start with a smaller but potentially incomplete encoding that can iteratively be adjusted to the complete encoding. We evaluate our method on a parameterized set of benchmarks encoding multi-robot delivery created from a graph abstraction of a hospital-like environment. The effectiveness of our approach is demonstrated using a range of encodings, including quantifier-free theories of uninterpreted functions and linear or bitvector arithmetic across multiple solvers.

This paper presents a novel framework enabling end-users to perform the management of complex robotic workplaces using a tablet and augmented reality. The framework allows users to commission the workplace comprising different types of robots, machines, or services irrespective of the vendor, set task-important points in space, specify program steps, generate a code, and control its execution. More users can collaborate simultaneously, for instance, within a large-scale workplace. Spatially registered visualization and programming enable a fast and easy understanding of workplace processes, while high precision is achieved by combining kinesthetic teaching with specific graphical tools for relative manipulation of poses. A visually defined program is for execution translated into Python representation, allowing efficient involvement of experts. The system was designed and developed in cooperation with a system integrator based on an offline printed circuit board testing use case, and its user interface was evaluated multiple times during the development. The latest evaluation was performed by three experts and indicates the high potential of the solution.

This post was published on April 1st, 2020, and should not be taken too seriously. You use new formulas, you gather insights, you vote and you have action points. Sometimes you start to regret being just a human. What if you could process incoming requests in parallel? And provide always most accurate responses?

How we adopt – and adapt to – artificial intelligence in the workplace will be crucial to the future of our region's economic growth and the very nature of employment. The 4th Industrial Revolution is already bringing many changes. And, policymakers, employers, and civil society organizations across the Asia Pacific are looking for innovative and inclusive ways to maximize the opportunities created by AI. They also want to manage the challenges. Among these is the need to understand what the evidence is telling us. In particular, governments want to see what the likely outcomes are so that they can invest today in policies to re-skill for an AI-driven future.