(Q6.1) Consider a 2-player, zero-sum x-o game, in a 3 x3 board. If the initial state of the game is: 0|0|x X0 X Sketch the game tree, given that the first move will be for the 'x' player. (Q6.2) If a win state occurs for a 3-length complete row, column, or diagonal of the same symbol. Use the Min-max algorithm to solve the game playing problem 'corresponding to player x if +2 is set to a win state, -2 is set for a lose, and 0 for neutral.

(Q6.1) Consider a 2-player, zero-sum x-o game, in a 3 x3 board. If the initial state of the game is: 0|0|x X0 X Sketch the game tree, given that the first move will be for the 'x' player. (Q6.2) If a win state occurs for a 3-length complete row, column, or diagonal of the same symbol. Use the Min-max algorithm to solve the game playing problem 'corresponding to player x if +2 is set to a win state, -2 is set for a lose, and 0 for neutral.

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Question 8 Greedy best-fırst search is equivalent to A* search with all step costs set to 0. O True O False Question 9 If you had implemented Uniform Cost Search (the graph search version) in Programming Assignment 1, it would have found an optimal solution. (You may assume that the path costs are kept with the nodes on the frontier and explored lists and checked when comparing newly generated states to what has been seen before.) O True O False Question 10 A* search with an admissible heuristic always expands fewer nodes than depth-first search. O True O False
Show the differences between breadth first search and best first search in 8 Puzzle game. Start and goal states are given below. Sequence of operation must be up, down, left, right for blank tile and heuristic function is number of tiles out of place. Maximum 4 levels may be searched.
17 B 5 3 7 6 18 14 15 12 2 8 10 1 16 11 13 J 9 I 15 H a. Consider the graph given above. Use Prim's and Kruskal's algorithm to find the minimum spanning tree. b. What is the total weight of the spanning trees? List the weights of the selected edges separated by commas in the order of selection.
Algebraic Preis’ AlgorithmAlgorithm due to Preis provides a different way to solve the maximal weightedmatching problem in a weighted graph. The algorithm consists of the followingsteps.1. Input: A weighted graph G = (V, E, w)2. Output: A maximal weighted matching M of G3. M ← Ø4. E ← E5. V ← V6. while E = Ø7. select at random any v ∈ V8. let e ∈ E be the heaviest edge incident to v9. M ← M ∪ e10. V ← V {v}11. E ← E \ {e and all adjacent edges to e}show two ways of implementing this algorithm in Python
Algebraic Preis’ AlgorithmAlgorithm due to Preis provides a different way to solve the maximal weightedmatching problem in a weighted graph. The algorithm consists of the followingsteps.1. Input: A weighted graph G = (V, E, w)2. Output: A maximal weighted matching M of G3. M ← Ø4. E ← E5. V ← V6. while E = Ø7. select at random any v ∈ V8. let e ∈ E be the heaviest edge incident to v9. M ← M ∪ e10. V ← V {v}11. E ← E \ {e and all adjacent edges to e} show two ways of implementing this algorithm in Python
Appendix A 10-Fold Cross Validation for Parameter Selection Cross Validation is the standard method for evaluation in empirical machine learning. It can also be used for parameter selection if we make sure to use the training set only. To select parameter A of algorithm A(X) over an enumerated range d E [A1,..., A] using dataset D, we do the following: 1. Split the data D into 10 disjoint folds. 2. For each value of A e (A1,..., Ar]: (a) For i = 1 to 10 Train A(A) on all folds but ith fold Test on ith fold and record the error on fold i (b) Compute the average performance of A on the 10 folds. 3. Pick the value of A with the best average performance Now, in the above, D only includes the training data and the parameter A is chosen without the knowledge of the test data. We then re-train on the entire train set D using the chosen A and evaluate the result on the test set.
17 There is a single pile of n chips. Two players take turns by removing from the pile at least one and at most m chips; the number of chips taken may vary from one move to another, but both the lower and upper limits stay the same. The winner is the last player who takes the last chip. Which algorithm design strategy is used in this game? Decrease-by-a-constant algorithm Divide-and-Conquer Variable-size-decrease algorithm Decrease-by-a-constant factor algorithm
Correct answer will be upvoted else downvoted. Today we will play a red and white shading game (no, this isn't the Russian Civil War; these are only the shades of the Canadian banner). You are given a n×m matrix of "R", "W", and "." characters. "R" is red, "W" is white and "." is clear. The neighbors of a cell are those that share an edge with it (those that main offer a corner don't count). Your responsibility is to shading the clear cells red or white so every red cell just has white neighbors (and no red ones) and each white cell just has red neighbors (and no white ones). You are not permitted to recolour currently hued cells. Input The primary line contains t (1≤t≤100), the number of experiments. In each experiment, the principal line will contain n (1≤n≤50) and m (1≤m≤50), the tallness and width of the network separately. The following n lines will contain the matrix. Each character of the matrix is either 'R', 'W', or '.'. Output For each experiment,…
(a) Consider the maze given below. 4 3 G 1 1 2 3 4 5 The black cells are obstacles. The problem is to find the shortest path from the start position S to the goal position G. You can move horizontally or vertically, and you need to avoid the obstacles. Identify an appropriate heuristic for A* search. Show the search tree generated by A* search, the shortest path, and the order of expanded nodes.
Algebraic Preis’ AlgorithmThe algorithm consists of the following steps.1. Input: A weighted graph G = (V, E, w)2. Output: A maximal weighted matching M of G3. M ← Ø4. E ← E5. V ← V6. while E = Ø7. select at random any v ∈ V8. let e ∈ E be the heaviest edge incident to v9. M ← M ∪ e10. V ← V {v}11. E ← E \ {e and all adjacent edges to e} show two ways of implementing this algorithm in Python. don't copy old barleby or chegg its wrong one
5.1. Trace the operation of Greedy Best-First Search, applied to the problem of getting to the goal state G from state S given below, for the following heuristic function: h,(N) = sum of the (Manhattan) distances of every tile to its goal position 341 25 876 State S 432 16 578 State G
Consider the following map and the straight-line distance to the city entitled “M” from every other city. Find the shortest path from “A” to “M” using A* searching algorithm. Draw the tree structure and trace algorithm using open and closed list. Also state the shortest route with cost. H(n) value of each city is given in the following table. To calculate the final f(n) of each city use the following formula except city M. g(n)=Straight line distance(n)+last two digits of your registration number i-e 58 F(n)=g(n) + H(n) Heuristic value H(n) of each city given in the following table A B C D E F G H I J K L M 223 222 166 192 165 136 122 111 100 60 32 102 0