[00:00] In this video, we are going to discuss the introduction to game playing. As human beings, we like game playing.
[00:12] our searching algorithms are used, rational mind and logic are used. Although we don't know the next step of the opponent.
[00:26] But somehow, we use our intelligence and try to beat the opponent. There are well-defined rules
[00:38] Either we will win or lose, or the match may draw. Due to no bias and usage of the searching-sorting algorithm, this topic has been the top priority of the researchers of artificial intelligence.
[01:00] The environment was multi-agent was first introduced in game playing only. which are popular currently, like PUBG, GTA, Call Of Duty, Counter Strike, etc.
[01:17] We are talking about the basic games in which we use our intelligence and logic On the basis of those rules only, we evaluate things.
[01:29] We also especially don't talk about those games When we use dice, our luck factor is also used.
[01:42] In cards also, which cards you get depends upon your luck. But somehow, there is the luck factor.
[01:54] In AI, we talk about the basic games What move I am making or what move the opponent is making?
[02:07] And what may be the result? We try to create a tree of these things in our minds. Some very popular games among these are chess,
[02:21] checkers, tic tac toe, nim game, and the 8 puzzle game. You will find so many games on which the researchers of artificial intelligence have worked a lot.
[02:33] IBM created a program that beat the world champion of chess in around 1996. The funda is that we want to create logic or intelligence in machines
[02:51] Why? Because they are using the rational mind. And as human beings, we can think 1-2 steps further. Like if the opponent does this, and if I do that, this may happen.
[03:05] But as a human being, our thinking is limited. But if we are putting that into machines, they are performing better as compared to human beings.
[03:18] If we talk from the point of view of artificial intelligence, minimax algorithm, and alpha-beta pruning. I have drawn a search graph of tic tac toe.
[03:30] I am bringing game-playing introductions so that you have a basic overview of the words that we use in game playing in artificial intelligence. Let's call it Game Tree
[03:44] We also call it Space Graph. It denotes the choices we have, Let's assume, that I playing as a max
[04:06] This is used in min and max. so that we can increase efficiency. I will tell you about these algorithms in detail.
[04:20] On the first level, max works. Max means that I am maximizing my probability of winning.
[04:32] And the other person, who is the opponent, is trying to minimize the probability of me winning. We play this game in this way. The funda of utility is the payoff.
[04:46] These are zero-sum games. we don't say that the winner is getting Rs.10 and if he wins the next time, he will get Rs.50,
[04:59] In zero-sum, we work on a consistent value. For example, if we talk from the point of view of winning, We will add 1 point for him.
[05:15] And if he loses, we give him -1 point. On the basis of utility, we find out how the minimax algorithm works on backtracking
[05:29] and how we increase efficiency using alpha-beta pruning. Although, you can apply DFS on this space search diagram. But if we use the breath fast search on this, obviously, we have so many choices.
[05:45] Due to that, the space search graph of the game tree will be very large. The larger the graph, the more difficult searching and traversing will be.
[06:00] its complexity is the order of B raised to power D, I have drawn the diagram of the tic tac toe game. If max starts, he has 9 choices.
[06:14] He can choose any of these 9. Then, min will further explore any 1 of these choices. Due to this, the graph will keep expanding.
[06:28] So the branches will keep increasing. We call it depth as well as ply. because on the basis of these, you can easily understand min, max, alpha, beta, etc.
[06:46] You should not cram. I will further explain how min-max works with a simple example, That's all for the introduction.
[06:59] so that while watching the next video, you remember all the points. Thank you!