Search algorithms are used in games to determine a strategy. Algorithms look for possibilities and choose the best move. There are several parameters to think about: speed, precision, complexity, etc. Scientists explore the links between the algorithms in the search tree for solving games and the subjective experience of playing several games in turns.

For a long time, zero-sum games for two people have been in the focus of researchers in various communities. The efforts were mainly due to a fascination with special competitions, such as Deep Blue against Kasparov, and for the beauty of lounge games such as Checkers, Backgammon, Othello and Go. Introduction to algorithms for playing computer games. We describe the assumptions about discrete zero-sum finite deterministic games for two players with perfect information.

We also practiced saying that substantive phrase in one breath. Once the recovery teams have done their job, we talk about solving those games with Minimax and then with an alpha-beta search. We also looked at the dynamic programming approach, which is most commonly used for game endings. We also discussed the theory and practice of heuristic evaluation functions in games.

In particular, this mental process is similar to the one that some search algorithms are designed for, not only for solving games, but also for general computer tasks in various areas of application. These indicators can unite search algorithms and entertainment using the analogy of “movement in the mind” to discover possible underlying affective experiences. Ultimately, search indicators and algorithms must be carefully designed to minimize the computing resources used; it is not necessary to consider in detail all possible strategies, but only those that are likely to win. When playing a game, for example, an AI based on a search algorithm would use search indicators to analyze possible future states and, at the same time, would mainly search for plays that would, in some way, maximize the chances of winning.

These search indicators are values that the algorithms in the search tree calculate to “evaluate” your progress towards a desired objective. The low cost of computing power and the ability to collect data on a large scale through the Internet have created new opportunities to implement these algorithms in hitherto impossible ways, and it is these types of algorithms that online platforms use to extract user data and convert it into fragments of value, such as digital shale oil.

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