Description

Sorts of Games. Backgammon. Imposing business model. Chess. Checkers. Go. Othello. Card Games. War vessel. Completely ... We will concentrate on deterministic, two-player, completely noticeable amusements ...

Transcripts

Minimax and Alpha-Beta Reduction Borrows from Spring 2006 CS 440 Lecture Slides

Motivation Want to make projects to play diversions Want to play ideally Want to have the capacity to do this in a sensible measure of time

Types of Games Nondeterministic (Chance) Deterministic Chess Checkers Go Othello Backgammon Monopoly Fully Observable Battleship Card Games Partially Observable Minimax is for deterministic, completely perceptible recreations

Basic Idea Search issue Searching a tree of the conceivable moves keeping in mind the end goal to discover the move that creates the best result Depth First Search calculation Assume the adversary is likewise playing ideally Try to ensure a win in any case!

Required Pieces for Minimax An underlying state The positions of the considerable number of pieces Whose turn it is Operators Legal moves the player can make Terminal Test Determines if a state is a last state Utility Function

Utility Function Gives the utility of an amusement state utility(State) Examples - 1, 0, and +1, for Player 1 loses, draw, Player 1 wins, individually Difference between the point aggregates for the two players Weighted total of elements (e.g. Chess) utility(S) = w 1 f 1 (S) + w 2 f 2 (S) + ... + w n f n (S) f 1 (S) = (Number of white rulers) – (Number of dark rulers), w 1 = 9 f 2 (S) = (Number of white rooks) – (Number of dark rooks), w 2 = 5 ...

Two Agents MAX Wants to amplify the consequence of the utility capacity Winning system if, on MIN\'s turn, a win is reachable for MAX for all moves that MIN can make MIN Wants to minimize the aftereffect of the assessment capacity Winning procedure if, on MAX\'s turn, a win is possible for MIN for all moves that MAX can make

Basic Algorithm

Example Coins diversion There is a pile of N coins In turn, players take 1, 2, or 3 coins from the stack The player who takes the last coin loses

Coins Game: Formal Definition Initial State: The quantity of coins in the stack Operators: 1. Expel one coin 2. Expel two coins 3. Evacuate three coins Terminal Test: There are no coins left on the stack Utility Function: F(S) F(S) = 1 if MAX wins, 0 if MIN wins

MAX MIN N = 4 K = 1 3 2 N = 3 K = N = 2 K = N = 1 K = 1 3 2 1 N = 0 K = N = 1 K = N = 2 K = N = 0 K = N = 1 K = N = 0 K = F(S)=1 1 2 F(S)=1 1 F(S)=1 N = 3 K = N = 1 K = N = 0 K = N = 0 K = F(S)=0 1 F(S)=0 N = 0 K = F(S)=1

Solution MAX MIN N = 4 K = 1 3 2 N = 3 K = 0 N = 2 K = 0 N = 1 K = 1 3 2 1 N = 0 K = 1 N = 1 K = 0 N = 2 K = 1 N = 0 K = 1 N = 1 K = 0 N = 0 K = 1 F(S)=1 1 2 F(S)=1 1 F(S)=1 N = 3 K = 0 N = 1 K = 1 N = 0 K = 0 N = 0 K = 0 F(S)=0 1 F(S)=0 N = 0 K = 1 F(S)=1

Analysis Max Depth: 5 Branch element: 3 Number of hubs: 15 Even with this unimportant illustration, you can see that these trees can get huge Generally, there are O(b d ) hubs to look for Branch element b: most extreme number of moves from every hub Depth d: greatest profundity of the tree Exponential time to run the calculation! In what capacity would we be able to make it quicker?

Alpha-Beta Pruning Main thought: Avoid handling subtrees that have no impact on the outcome Two new parameters α: The best esteem for MAX seen so far β: The best esteem for MIN seen so far α is utilized as a part of MIN hubs, and is alloted in MAX hubs β is utilized as a part of MAX hubs, and is allocated in MIN hubs

Alpha-Beta Pruning MAX (Not at level 0) If a subtree is found with a worth k more noteworthy than the estimation of β, then we don\'t have to keep looking subtrees MAX can do in any event in the same class as k in this hub, so MIN could never go here! MIN If a subtree is found with a worth k not exactly the estimation of α, then we don\'t have to keep looking subtrees MIN can do at any rate in the same class as k in this hub, so MAX could never go here!

Algorithm

MAX MIN N = 4 K = α = β = 1 3 α = β = 2 N = 3 K = α = β = N = 2 K = α = β = N = 1 K = 1 α = β = α = β = 3 2 1 N = 0 K = N = 1 K = α = β = N = 2 K = α = β = N = 0 K = N = 1 K = α = β = N = 0 K = F(S)=1 1 2 F(S)=1 1 F(S)=1 α = β = N = 3 K = N = 1 K = N = 0 K = N = 0 K = α = β = α = β = α = β = α = β = F(S)=0 1 F(S)=0 N = 0 K = α = β = F(S)=1

MAX MIN N = 4 K = 0 1 α = 0 β = 1 3 α = 0 β = 1 2 N = 3 K = 1 0 α = β = 1 0 N = 2 K = 1 0 α = 0 β = 0 N = 1 K = 1 α = 0 β = α = 1 β = 3 2 1 N = 0 K = 1 N = 1 K = 0 α = 0 β = 1 0 N = 2 K = 1 α = β = 0 N = 0 K = 1 N = 1 K = α = 0 β = 0 N = 0 K = 1 F(S)=1 1 2 F(S)=1 1 F(S)=1 α = 0 β = 1 N = 3 K = 0 N = 1 K = 1 N = 0 K = N = 0 K = 0 α = 0 β = α = 1 β = 0 α = β = α = 0 β = 0 F(S)=0 1 F(S)=0 N = 0 K = 1 α = 1 β = 0 F(S)=1

Nondeterministic Games Minimax can likewise be utilized for nondeterministic amusements (those that have a component of chance) There is an extra hub included (Random hub) Random hub is amongst MIN and MAX (and the other way around) Make subtrees over the greater part of the possibilities,and normal the outcomes

Example Weighted coin .6 Heads (1) .4 Tails (0) N = 2 K = 8.6 Random Node K = .4*5 + .6*11 = 8.6 K = .4*2 + .6*7 = 5 0 1 0 1 K = 5 K = 11 K = 2 K = 7

Our Project We will concentrate on deterministic, two-player, completely perceptible recreations We will attempt to take in the evaluator capacity, so as to spare time when playing the diversion Training on information from Minimax runs (Neural Network) Having the system play against itself (Genetic Algorithms)

Conclusion Minimax finds ideal play for deterministic, completely recognizable, two-player amusements Alpha-Beta decrease makes it quicker