We provide a learning-based technique for guessing a winning strategy in a parity game originating from an LTL synthesis problem. A cheaply obtained guess can be useful in several applications. Not only can the guessed strategy be applied as best-effort in cases where the game's huge size prohibits rigorous approaches, but it can also increase the scalability of rigorous LTL synthesis in several ways. Firstly, checking whether a guessed strategy is winning is easier than constructing one. Secondly, even if the guess is wrong in some places, it can be fixed by strategy iteration faster than constructing one from scratch. Thirdly, the guess can be used in on-the-fly approaches to prioritize exploration in the most fruitful directions. In contrast to previous works, we (i) reflect the highly structured logical information in game's states, the so-called semantic labelling, coming from the recent LTL-to-automata translations, and (ii) learn to reflect it properly by learning from previously solved games, bringing the solving process closer to human-like reasoning.

We've done some extensive research to help illustrate a comparison of traditional manual labor vs automation in the infographic below. We illustrate this example by comparing a production application where the traditional solution to production is adding people to increase output. We compare it to the opportunity of integrating an automated solution and the potential for payback or the Return on Investment (ROI). In this scenario, our customer was using 8 production workers x 2 shifts per day to meet their production goals. This was challenging in a tight labor market and our customer was frequently faced with a shortage of labor.

The smart parking industry continues to evolve as an increasing number of cities struggle with traffic congestion and inadequate parking availability. While the deployment of sensor technologies continues to be core to the development of smart parking, a wide variety of other technology innovations are also enabling more adaptable systems--including cameras, wireless communications, data analytics, induction loops, smart parking meters, and advanced algorithms. The future of the smart parking market is expected to be significantly influenced by the arrival of automated vehicles (AVs). Several cities around the world are already beginning to trial self-parking vehicles, specialized AV parking lots, and robotic parking valets. For example, in Boulder, Colorado, ParkPlus is working on deploying the first fully automated parking garage in the Western United States through Boulder's PearlWest mixed-use development.

Washington, Dec 26: Researchers have developed an automated technology that combines imaging with digital analysis and machine learning to help physicians detect melanoma at its early stages. People with melanoma often have mole-looking growths on their skin that tend to be irregular in shape and colour and can be hard to tell apart from benign ones, making the disease difficult to diagnose. "There is a real need for standardisation across the field of dermatology in how melanomas are evaluated," said James Krueger, Professor at Rockefeller University in the US. "Detection through screening saves lives but is very challenging visually, and even when a suspicious lesion is extracted and biopsied, it is confirmed to be melanoma in only about 10 per cent of cases," said Krueger. In the new approach, images of lesions are processed by a series of computer programmes that extract information about the number of colours present in a growth and other quantitative data.