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CS607 HANDOUTS PDF

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Link to this post 22 Nov Download VU Artificial Intelligence – CS VU Lecture Handouts. Artificial Intelligence – CS Handouts Lecture 1 – · Artificial. Artificial Intelligence – CS VU Video Lectures. Watch Online Artificial Intelligence – CS VU Video Lectures. Artificial Intelligence – CS VU Lecture. Artificial Intelligence CS Download Complete Lectures Artificial VU Projects · Video Lectures · Handouts · Past Papers · Quizzes.

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We select H which is the best of them.

Such scenarios usually occur in game playing where two opponents also called adversaries are searching for a goal. The values on the nodes shown in yellow are the underestimates of the distance of a specific node from G. The minimizer has to keep in view that what choices will be available to the maximizer on the next step. We then move to F as that is the best option at this point with a value 7. Support your vs607 with examples of a few trees.

Now A and E are equally good nodes so we arbitrarily choose amongst them, and hamdouts move to A. We start with a tree with goodness of every node mentioned on it. One such procedure is called branch-and-bound method.

Standing at S we observe that the best node is A with bandouts value of 4 so we move to 4. Hence we handohts all the further sub-trees along this path, as shown in the diagram below. Both have their advantages and disadvantages. So we ignore any further paths ahead of the path S D A B. Hence best first search is a greedy approach will looks for the best amongst the available options and hence can sometimes reduce the searching time.

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Full text of “Artificial Intelligence CS Handouts Lecture 9 10”

We will demonstrate this improvement with an example. Hence we always travel with underestimates of the remaining distance. Suggest Improvements in the Algorithm. Handout Discuss the problems in Hill Climbing.

Q3 Given the following tree. At last from H we find Hndouts as the best. The simple idea behind dynamic programming is that if we can reach a specific node through more than one different path then we shall take the path with the minimum cost. The length of the complete path from S to G is 9.

Artificial Intelligence – CS607 VU Video Lectures

Q6 Discuss how best first search works in a tree. Given the following tree, use the hill climbing procedure to climb up the tree. Many games can be modeled as trees as shown below. So traveling further from S D A B to some other node will make the path longer. Also note that while traveling from S to B we have already covered a distance of 9 units.

Try to model the problem in a graphical representation. Now, since the choice is between scores of 3 or 2, the maximizer will go to node B from A.

We visit F and finally we reach G as shown in the subsequent diagrams. Hence maximizer will end up with a score of 2 if he goes to C from A. The maximizer wishes to maximize the score so apparently 7 being the maximum score, the maximizer handouhs go to C and then to G. As all the sub-trees emerging from B make our path length more than 9 units so we bound this path, as shown in the next diagram. We construct the tree corresponding to the graph above.

Search the history of over billion web pages on the Internet. The static evaluation scores for each leaf node are written under it.

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For example, in a game of ce607 player one might want that he should complete a line with crosses while at the same time player two wants to complete a line of zeros.

The numbers on the nodes are the estimated distance on the node from the goal state. Fortunately handous is a procedure that reduces both the tree branches that must be generated and the number of evaluations. The second improvement is dynamic programming.

Artificial Intelligence – CS VU Lecture Handouts

All these heuristically informed procedures are considered better but they do not guarantee the optimal solution, as they are dependent on the quality of heuristic being used. The player hoping for positive numbers is called maximizing player or maximizer. We see that C is a leaf node so we bind C too as shown in the next diagram. Suppose we start of with a game tree in the diagram below. Here we assume that we have a situation analyzer that converts all judgments about board situations into a single, over all quality number.

Starting at S handoits see that A is the best option so we explore A. For example the static evaluation scores for the left most leaf node is Run the MiniMax procedure on the given tree.

Will it always guarantee the best solution? We will focus on board games for simplicity The rode is a game tree represent board configuration and the branches indicate how moves can connect them.