1598458867

STEP 1 : Constructing “tree”

When we start the game, each player have 16 pieces. Assume now is computer turn. The computer can make 1 of 20 possible moves (2 each for the 8 pawn, plus 2 each for the knights). After that, the opponent can also make 20 possible moves. That make it we have 20*20 possible scenarios which means 400 scenarios only in two turns. Next, the computer can make another 20 possible moves to each of these 400 scenarios.

As long the game still continues, the tree will keeps growing. In theory, the perfect computer would be able to get to the very bottom of this tree, and look at all possible configurations of the board, approximately 10¹²⁰. Then, it would see which are the paths down this tree that will lead to the victory, and choose the best one for the bot.

STEP 2 : Evaluating the outcomes

Pretty sure that all of us know the problem, 10¹²⁰ is very huge number. For your information, the total estimated atoms in the universe are 10⁷⁵, in other words, the bot might still calculating its move while universe already reached its end.

The real and sufficient computers will build up this tree to the best of their hardware capabilites, like 5, or 10, or 20 or whatever moves into the future. Once they have this limited tree, they evaluate each position using an evaluation function.

The pretty simple example is an evaluation function could be the number of pieces the computer has minus number of pieces opponent has. For example, the computer has 12 pieces left on the board, while the opponent only has 8. Then the computer would evaluate such a board to 12-8 = 4.

Of course, that is not a very good evaluation function, but that is the idea. This can be made more and more complicated, taking into account of many values such as individual pieces, board position, control of the center, vulnerability of the king to check, vulnerability of the opponent’s queen, and tons of other parameters. Function can be whatever as long as it works, as it allows a computer to compare board positions, to see which are the desirable outcomes.

STEP 3 : Making a move

After doing the analysis, now it is time to make the decision, this is the example of simplified tree.Now, let’s begin the fun part.First, it starts from the most bottom level, let’s call it first level. The computer will chooses the one with maximum score. Consider the right-most square team at the first level. It has two possible outcomes 2 and 5. Since it will be the computer’s turn at that stage, it chooses the best outcome, the one with MAX score, which is 5, and so it assigns 5 to that node. Same things for other square in the same level.

#mathematics #programming #math #chess #algorithms

1598458867

STEP 1 : Constructing “tree”

When we start the game, each player have 16 pieces. Assume now is computer turn. The computer can make 1 of 20 possible moves (2 each for the 8 pawn, plus 2 each for the knights). After that, the opponent can also make 20 possible moves. That make it we have 20*20 possible scenarios which means 400 scenarios only in two turns. Next, the computer can make another 20 possible moves to each of these 400 scenarios.

As long the game still continues, the tree will keeps growing. In theory, the perfect computer would be able to get to the very bottom of this tree, and look at all possible configurations of the board, approximately 10¹²⁰. Then, it would see which are the paths down this tree that will lead to the victory, and choose the best one for the bot.

STEP 2 : Evaluating the outcomes

Pretty sure that all of us know the problem, 10¹²⁰ is very huge number. For your information, the total estimated atoms in the universe are 10⁷⁵, in other words, the bot might still calculating its move while universe already reached its end.

The real and sufficient computers will build up this tree to the best of their hardware capabilites, like 5, or 10, or 20 or whatever moves into the future. Once they have this limited tree, they evaluate each position using an evaluation function.

The pretty simple example is an evaluation function could be the number of pieces the computer has minus number of pieces opponent has. For example, the computer has 12 pieces left on the board, while the opponent only has 8. Then the computer would evaluate such a board to 12-8 = 4.

Of course, that is not a very good evaluation function, but that is the idea. This can be made more and more complicated, taking into account of many values such as individual pieces, board position, control of the center, vulnerability of the king to check, vulnerability of the opponent’s queen, and tons of other parameters. Function can be whatever as long as it works, as it allows a computer to compare board positions, to see which are the desirable outcomes.

STEP 3 : Making a move

After doing the analysis, now it is time to make the decision, this is the example of simplified tree.Now, let’s begin the fun part.First, it starts from the most bottom level, let’s call it first level. The computer will chooses the one with maximum score. Consider the right-most square team at the first level. It has two possible outcomes 2 and 5. Since it will be the computer’s turn at that stage, it chooses the best outcome, the one with MAX score, which is 5, and so it assigns 5 to that node. Same things for other square in the same level.

#mathematics #programming #math #chess #algorithms

1614329473

G Suite is one of the Google products, developed form of Google Apps. It is a single platform to hold cloud computing, collaboration tools, productivity, software, and products. While using it, many a time, it’s not working, and users have a question– **How to fix G Suite not working on iPhone**? It can be resolved easily by restarting the device, and if unable to do so, you can reach our specialists whenever you want.

**For more details:** https://contactforhelp.com/blog/how-to-fix-the-g-suite-email-not-working-issue/

#g suite email not working #g suite email not working on iphone #g suite email not working on android #suite email not working on windows 10 #g suite email not working on mac #g suite email not syncing

1607930471

Xfinity, the tradename of Comcast Cable Communications, LLC, is the first rate supplier of Internet, satellite TV, phone, and remote administrations in the United States. Presented in 2010, previously these administrations were given under the Comcast brand umbrella. Xfinity makes a universe of mind boggling amusement and innovation benefits that joins a great many individuals to the encounters and minutes that issue them the most. Since Xfinity is the greatest supplier of link administrations and home Internet in the United States, it isn’t amazing that the organization gets a ton of investigating and inquiry goal demands on its telephone based Xfinity Customer Service.

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1624970880

As more data, better algorithms, and higher computing power continue to shape the future of artificial intelligence (AI), reliable machine learning models have become paramount to optimise outcomes. OpenAI’s meta-learning algorithm, Reptile, is one such model designed to perform a wide array of tasks.

For those unaware, meta-learning refers to the idea of ‘learning to learn by solving multiple tasks, like how humans learn. Using meta-learning, you can design models that can learn new skills or adapt to new environments rapidly with a few training examples.

In the recent past, the meta-learning algorithm has had a fair bit of success as it can learn with limited quantities of data. Unlike other learning models like reinforcement learning, which uses reward mechanisms for each action, meta-learning can generalise to different scenarios by separating a specified task into two functions.

The first function often gives a quick response within a specific task, while the second function includes the extraction of information learned from previous tasks. It is similar to how humans behave, where they often gain knowledge from previous unrelated tasks or experiences.

Typically, there are three common approaches to meta-learning.

- Metric-based: Learn an efficient distance metric
- Model-based: Use (recurrent) network with external or internal memory
- Optimisation-based: Optimise the model parameters explicitly for fast learning

For instance, the above image depicts the model-agnostic meta-learning algorithm (MAML) developed by researchers at the University of California, Berkeley, in partnership with OpenAI. The MAML optimises for a representation θ that can quickly adapt to new tasks.

On the other hand, Reptile utilises a stochastic gradient descent (SGD) to initialise the model’s parameters instead of performing several computations that are often resource-consuming. In other words, it also reduces the dependency of higher computational hardware requirements, if implemented in a machine learning project.

#developers corner #how reptile works #meta learning algorithm #meta-learning algorithm #algorithm

1593347004

The Greedy Method is an approach for solving certain types of optimization problems. The greedy algorithm chooses the optimum result at each stage. While this works the majority of the times, there are numerous examples where the greedy approach is not the correct approach. For example, let’s say that you’re taking the greedy algorithm approach to earning money at a certain point in your life. You graduate high school and have two options:

#computer-science #algorithms #developer #programming #greedy-algorithms #algorithms