In this article, to answer these questions, we go through the Monte Carlo Tree Search fundamentals. Since in the next articles, we will implement this algorithm on "HEX" board game, I try to explain the concepts through examples in this board game environment. If you're more interested in the code, find it in this link. There is also a more optimized version which is applicable on linux due to utilizing cython and you can find it in here. Monte Carlo method was coined by Stanislaw Ulam for the first time after applying statistical approach "The Monte Carlo method".

Hierarchical planning has attracted renewed interest in the last couple of years. As a consequence, the time was right to establish a workshop devoted entirely to hierarchical planning – an insight shared by many supporters. In this paper we report on the first ICAPS workshop on Hierarchical Planning held in Delft, The Netherlands, in 2018 as well as on the second workshop held in Berkeley, CA, USA, in 2019. Hierarchical planning approaches incorporate hierarchies in the domain model. In the most common form, the hierarchy is defined among tasks, leading to the distinction between primitive and abstract tasks.

The claim: A standard housing development takes an average of five years to go through the planning system. Verdict: Developments of more than 1,000 dwellings may take that long, but those are very large. The vast majority of developments are much smaller and do not take that long. Robert Jenrick, secretary of state for housing, communities and local government, wrote an article in the Sunday Telegraph talking about how he wants to change the planning system in England. In the article, he wrote: "Under the current system, it takes an average of five years for a standard housing development to go through the planning system - before a spade is even in the ground."

Sweeping changes to England's planning system will "cut red tape, but not standards," Housing Secretary Robert Jenrick has said. Under draft new laws, first revealed on Sunday, developers will be granted "automatic" permission to build homes and schools on sites for "growth". It follows Boris Johnson's pledge to "build back better" after coronavirus. But critics warn it could lead to "bad-quality housing" and loss of local control over development. Mr Johnson promised to speed up investment into homes and infrastructure in June to help the UK recover from the economic impact of coronavirus.

Attainia, Inc., the market leader in cloud-based software for medical equipment planning, announced the integration of its flagship planning software with Accruent Data Insights, a powerful analytics solution that aggregates asset ownership data for thousands of equipment models. This new feature available now will allow healthcare organizations working in Attainia's PLAN-IT suite of products to optimize their purchasing decisions and control capital asset costs by viewing reliability and total cost of ownership (TCO) data during the equipment planning process. "The integration of Accruent's Data Insights module into Attainia's Plan-IT suite delivers powerful capabilities to reimagine how projects are planned and equipment is specified," said DJ Chhabra, CEO and chairman of Attainia. "The ability to gain insights into the life expectancy and TCO of equipment during the planning process, and the ability to plan for a replacement well before it's needed, allows healthcare organizations to be proactive -- which becomes paramount to improving patient satisfaction, protecting a hospital's revenue stream and delivering overall efficiencies in the management of capital equipment." Most hospitals have a mandate to drive cost reduction, but the lack of actionable data and resource constraints makes this an extremely complex challenge.

At the American Association of Physicists in Medicine (AAPM) 2019 meeting, new artificial intelligence (AI) software to assist with radiotherapy treatment planning systems was highlighted. The goal of the AI-based systems is to save staff time, while still allowing clinicians to do the final patient review. RaySearch demonstrated a new U.S. Food and Drug Administration (FDA)-cleared machine learning treatment planning system. The RaySearch RayStation machine learning algorithm is being used clinically by University Health Network, Princess Margaret Cancer Center, Toronto, Canada, where it was rolled out over several months in late-2019. Medical physicist Leigh Conroy, Ph.D., was involved in this rollout and helped conduct a study, showing the automated plans and traditionally made plans to radiation oncologists to get valuable feedback.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Big Ten's plan to have a conference-only schedule during the college football season stunned many fans and left at least one U.S. senator upset. Sen. Chuck Grassley, R-Iowa, tweeted his disappointment Thursday after learning one of the top conferences in college football was changing up its schedule as the U.S. continues to battle the coronavirus pandemic with no real end in sight. Grassley was particularly upset that there would be no rivalry game between Iowa and Iowa State this year.

At the American Association of Physicists in Medicine (AAPM) 2019 meeting, new artificial intelligence (AI) software to assist with radiotherapy treatment planning systems was highlighted. The goal of the AI-based systems is to save staff time, while still allowing clinicians to do the final patient review. RaySearch demonstrated a new U.S. Food and Drug Administration (FDA)-cleared machine learning treatment planning system. The RaySearch RayStation machine learning algorithm is being used clinically by University Health Network, Princess Margaret Cancer Center, Toronto, Canada, where it was rolled out over several months in late-2019. Medical physicist Leigh Conroy, Ph.D., was involved in this rollout and helped conduct a study, showing the automated plans and traditionally made plans to radiation oncologists to get valuable feedback.

Monte-Carlo planning and Reinforcement Learning (RL) are essential to sequential decision making. The recent AlphaGo and AlphaZero algorithms have shown how to successfully combine these two paradigms in order to solve large scale sequential decision problems. These methodologies exploit a variant of the well-known UCT algorithm to trade off exploitation of good actions and exploration of unvisited states, but their empirical success comes at the cost of poor sample-efficiency and high computation time. In this paper, we overcome these limitations by considering convex regularization in Monte-Carlo Tree Search (MCTS), which has been successfully used in RL to efficiently drive exploration. First, we introduce a unifying theory on the use of generic convex regularizers in MCTS, deriving the regret analysis and providing guarantees of exponential convergence rate. Second, we exploit our theoretical framework to introduce novel regularized backup operators for MCTS, based on the relative entropy of the policy update, and on the Tsallis entropy of the policy. Finally, we empirically evaluate the proposed operators in AlphaGo and AlphaZero on problems of increasing dimensionality and branching factor, from a toy problem to several Atari games, showing their superiority w.r.t. representative baselines.

The modern space race is heating up, and the European Union is acutely aware that it needs to keep pace. Space chief Thierry Breton told Reuters in an interview that the EU is accelerating its plans in light of rapid progress by private companies like SpaceX as well as China's successes. It's moving the deployment of its Galileo navigation satellites ahead by three years, to 2024, and will use its budget for the first time to support reusable rockets and other new launch tech. The EU is also forging a €1 billion deal with Arianespace to spur innovation, and will propose a €1 billion European Space Fund and competitions to foster startups. Breton also hoped to launch a pan-European satellite broadband network as well as a system to avoid collisions with satellites and other items in orbit.