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How Search Algorithms Reflect Game Playing?

by ChrisGreenwalty
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Search Algorithms Reflect Game Playing

Human beings are involved in playing games and getting benefit from them in different ways. There is a relaxed approach of gaming for getting pleasure as well as it takes the presence of mind and decision making skills to play well. It takes statistical and mathematical consideration to know the best moves while playing a game. It shows how active a human brain works and finds out the ways to cope with critical situations as well as to excel in whatever the current situation is. These games that take the working of brain and search algorithms make the person a well-played researcher on artificial intelligence.

With the passage of time, more critical and scenario based games are liked by players that require problem solving and decision making skills. The players who are experienced can get the idea easily that which move or turn can make them able to reach at the top while having certain tactics and smart moves in a particular situation of the game. It helps them out the make proper strategies and excel accordingly. This mental process is similar to the designing of some search algorithms. These are not just for solving and playing game but also in computer tasks in different aspects and numerous applications.

If we talk about gaming facts and the growing research about gaming, we will be surprised to know that there has been a considerable research done in this field. At JAIST (Japan Advanced Institute of Science and Technology), a professor named Hiroyuki Iida conducted a research, and his research group is working for the development of new theories for analysing and understating different sides of games and game playing. It is regarding both points of view; psychological and purely objective views. In his latest study along with first writer of a specific paper, tried to make connection between computing notions and experience of game playing. For doing this, they generated two types of algorithms that will be used in search algorithms.

One of them is probability based proof number that is called as PPN. Other one is single conspiracy number that is to be said as SCN. They applied both of them to several turn based games.  To understand in easier words, these search indicators are actually the values that are to be calculated by search algorithms for assessing their progress regarding an objective that is needed or desired. For example, when someone plays a game, an artificial intelligence based on search algorithms will use search indicators for analysing the states of potential future when they mainly look for the plays that enhances or add the chances of winning the game.

So, the indicators and search algorithms must be created with care in order to lessen the computing resources used. It is not necessary to consider each possible play in detail, but only those should be considered that are more likely to win. As far the context of different games is concerned, the researchers have applied these two indicators in the frameworks of search algorithms. These different games include Chess, Othello, Chinese Chess and 2028 (how interesting these games sound, aren’t they?).

The results have revealed some interesting information regarding what is there by each indicator. Professor Iida explained that the PPN (probability based proof number) based search gives the ways to determine what is the quality of information that is to be available in the game and that seems to operate in a likewise fashion to human instinct. On the contrary side, the SCN (single conspiracy number) based search gives a platform for understanding the experience of a player, and how they deal and manage with the risk when they take decisions. At Iida’s lab, there was a SCN based search approach linked with another framework that was theoretical.

According to a dissertation help firm, this theoretical frame work was ‘the concept of motion in mind’. This approach was used to analyse different subjective as well as objective aspects of the experience of game playing in a mathematical fashion. It was done by producing the analogies with concepts of motion related aspects from physics. An aspect in classical mechanics is an example. The researchers compared the SCN with the analogies (analogies of motion in the game). The researchers concluded that underlying computations have a direct relation with oscillations that are from losing to wining positions in the game. It happens in the games that are either played by a single or by two player competition.

Both of the search approaches that were analysed in the study carried out by experts, have application in both of the empires of the game; inside and outside kingdom. For example, probability based proof number can be used for saving the valuable resources as well as the time during in-depth computing tasks. These tasks include optimizing the problems, schedule and plan. Meanwhile, single conspiracy number is helpful in a situation where there is a need to make high task decisions or when there is a necessity of long term planning. It allows the optimization of the values as well as to minimizing the risks that may come.

There are other multidisciplinary studies that can help us in finding more connections between information science, entertainment and the mind of a human. It is hoped that in the long run, we will be capable of modifying the games from a perspective that is more subjective or that is even purpose driven. It will enhance the enjoyment while being helpful in different other possible ways.

As a considerable amount of work has been done on gaming, still researchers and experts are trying to figure out the improved and advanced aspects that can be fruitful in the future of gaming for making it a strong zone. We are hopeful to see and enjoy more features of search algorithms in gaming and more advanced technical aspects that won’t be less than a treasure for those who are serious or professional gamers or who seek search algorithms as an interest for making their future in gaming.

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