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Search Results For Total War

The authors would like to thank Francesca Tripodi and Jenny Fan for sharing their insights and research, Chris Meserole for his invaluable feedback, Joseph Bodnar for his contributions to editing, and Rachel Slattery for layout. The authors are also grateful to NewsGuard, which provided data to support our analysis.

Search results for Total War

In computer science, Monte Carlo tree search (MCTS) is a heuristic search algorithm for some kinds of decision processes, most notably those employed in software that plays board games. In that context MCTS is used to solve the game tree.

The Monte Carlo method, which uses random sampling for deterministic problems which are difficult or impossible to solve using other approaches, dates back to the 1940s.[5] In his 1987 PhD thesis, Bruce Abramson combined minimax search with an expected-outcome model based on random game playouts to the end, instead of the usual static evaluation function. Abramson said the expected-outcome model "is shown to be precise, accurate, easily estimable, efficiently calculable, and domain-independent."[6] He experimented in-depth with tic-tac-toe and then with machine-generated evaluation functions for Othello and chess.

Such methods were then explored and successfully applied to heuristic search in the field of automated theorem proving by W. Ertel, J. Schumann and C. Suttner in 1989,[7][8][9] thus improving the exponential search times of uninformed search algorithms such as e.g. breadth-first search, depth-first search or iterative deepening.

In 2006, inspired by these predecessors,[14] Rémi Coulom described the application of the Monte Carlo method to game-tree search and coined the name Monte Carlo tree search,[15] L. Kocsis and Cs. Szepesvári developed the UCT (Upper Confidence bounds applied to Trees) algorithm,[16] and S. Gelly et al. implemented UCT in their program MoGo.[17] In 2008, MoGo achieved dan (master) level in 99 Go,[18] and the Fuego program began to win against strong amateur players in 99 Go.[19]

In January 2012, the Zen program won 3:1 in a Go match on a 1919 board with an amateur 2 dan player.[20] Google Deepmind developed the program AlphaGo, which in October 2015 became the first Computer Go program to beat a professional human Go player without handicaps on a full-sized 19x19 board.[1][21][22] In March 2016, AlphaGo was awarded an honorary 9-dan (master) level in 1919 Go for defeating Lee Sedol in a five-game match with a final score of four games to one.[23] AlphaGo represents a significant improvement over previous Go programs as well as a milestone in machine learning as it uses Monte Carlo tree search with artificial neural networks (a deep learning method) for policy (move selection) and value, giving it efficiency far surpassing previous programs.[24]

The focus of MCTS is on the analysis of the most promising moves, expanding the search tree based on random sampling of the search space.The application of Monte Carlo tree search in games is based on many playouts, also called roll-outs. In each playout, the game is played out to the very end by selecting moves at random. The final game result of each playout is then used to weight the nodes in the game tree so that better nodes are more likely to be chosen in future playouts.

This graph shows the steps involved in one decision, with each node showing the ratio of wins to total playouts from that point in the game tree for the player that the node represents.[37] In the Selection diagram, black is about to move. The root node shows there are 11 wins out of 21 playouts for white from this position so far. It complements the total of 10/21 black wins shown along the three black nodes under it, each of which represents a possible black move. Note that this graph does not follow the UCT algorithm described below.

This basic procedure can be applied to any game whose positions necessarily have a finite number of moves and finite length. For each position, all feasible moves are determined: k random games are played out to the very end, and the scores are recorded. The move leading to the best score is chosen. Ties are broken by fair coin flips. Pure Monte Carlo Game Search results in strong play in several games with random elements, as in the game EinStein würfelt nicht!. It converges to optimal play (as k tends to infinity) in board filling games with random turn order, for instance in the game Hex with random turn order.[38] DeepMind's AlphaZero replaces the simulation step with an evaluation based on a neural network.[2]

Most contemporary implementations of Monte Carlo tree search are based on some variant of UCT that traces its roots back to the AMS simulation optimization algorithm for estimating the value function in finite-horizon Markov Decision Processes (MDPs) introduced by Chang et al.[11] (2005) in Operations Research. (AMS was the first work to explore the idea of UCB-based exploration and exploitation in constructing sampled/simulated (Monte Carlo) trees and was the main seed for UCT.[12])

In particular, pure Monte Carlo tree search does not need an explicit evaluation function. Simply implementing the game's mechanics is sufficient to explore the search space (i.e. the generating of allowed moves in a given position and the game-end conditions). As such, Monte Carlo tree search can be employed in games without a developed theory or in general game playing.

A disadvantage is that in certain positions, there may be moves that look superficially strong, but that actually lead to a loss via a subtle line of play. Such "trap states" require thorough analysis to be handled correctly, particularly when playing against an expert player; however, MCTS may not "see" such lines due to its policy of selective node expansion.[42][43] It is believed that this may have been part of the reason for AlphaGo's loss in its fourth game against Lee Sedol. In essence, the search attempts to prune sequences which are less relevant. In some cases, a play can lead to a very specific line of play which is significant, but which is overlooked when the tree is pruned, and this outcome is therefore "off the search radar".[44]

Monte Carlo tree search can use either light or heavy playouts. Light playouts consist of random moves while heavy playouts apply various heuristics to influence the choice of moves.[45] These heuristics may employ the results of previous playouts (e.g. the Last Good Reply heuristic[46]) or expert knowledge of a given game. For instance, in many Go-playing programs certain stone patterns in a portion of the board influence the probability of moving into that area.[17] Paradoxically, playing suboptimally in simulations sometimes makes a Monte Carlo tree search program play stronger overall.[47]

The basic Monte Carlo tree search collects enough information to find the most promising moves only after many rounds; until then its moves are essentially random. This exploratory phase may be reduced significantly in a certain class of games using RAVE (Rapid Action Value Estimation).[48] In these games, permutations of a sequence of moves lead to the same position. Typically, they are board games in which a move involves placement of a piece or a stone on the board. In such games the value of each move is often only slightly influenced by other moves.

Clicks on paid search listings beat out organic clicks by nearly a 2:1 margin for keywords with high commercial intent in the US. In other words, 64.6% of people click on Google Ads when they are looking to buy an item online!

A special thank you to my colleagues, Miranda Miller (Search Engine Watch), Aaron Wall (SEO Book), Tom Demers (Measured SEM), AJ Kohn (Blind Five Year Old), and Elisa Gabbert (WordStream), all of whom provided incredibly valuable input and commentary into the design and data of this research study.

Our survey was limited to advertisers in the US, for Google Search only. In our survey, we define high commercial intent keywords specifically as keyword searches on Google that have significant advertiser competition and trigger a Google Shopping or Google Product Listing ad.

Because people often click on multiple ads and/or organic search listings from a single search result page (which makes the click-through rates of all of the paid and organic listings add up to more than 100%), we normalized the CTR data to reflect the % share of traffic generated for each paid and organic search listing present on a typical search engine results page.

This chart and the one below are based on research done by Provost Marshal General James Fry in 1866. His estimates for Southern states were based on Confederate muster rolls--many of which were destroyed before he began his study--and many historians have disputed the results. The estimates for Virginia, North Carolina, Alabama, South Carolina, and Arkansas have been updated to reflect more recent scholarship.

You can update how often you want to receive emails with your saved search results. Click Edit notifications settings to change your notifications to daily, weekly, monthly or turn them off.

You can only delete saved searches from your archived list. Go to your archived saved searches and click the + next to the saved search you want to delete. Click Delete located under the Unarchive Search button.

DuckDuckGo uses its web crawler, DuckDuckBot, and up to 400 other sources to compile its search results, including other search engines like Bing, Yahoo, and Yandex, and crowdsourcing sites like Wikipedia.

For instance, once a user has reached the bottom of a SERP, they can select to see more results and activate an endless scroll, which opens up the next SERP directly below the current one without opening a new page.

Google Maps is a powerful asset for the search engine. It boasts a plethora of significant information for businesses across the world, from names, addresses, and phone numbers to business-related photos, videos, reviews, and more. 041b061a72

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