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We convert the map to a tree as shown below.

Consider the following diagram. Fortunately there is a procedure that reduces both the tree branches that must be generated and the number of evaluations.

He has to drive on his car but doesn’t know the way to air port. Notice further that if player one puts a cross in any box, player-two will intelligently try to make a move that would leave player-one with minimum chance to win, that is, he will try to stop player- one from completing a line of crosses and at the same time will try to complete his line of zeros.

When he evaluates the first leaf node ce607 the other side of the tree, he will see that the minimizer can force him to a score of less than 3 hence there is no need to fully explore the tree from that side. Hence we use the estimates of the remaining distance.

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. But one thing that lacks in both is that whenever they find a solution they immediately stop. Hence we block all the further sub-trees along this path, as shown in the diagram below. The minimizer has to keep in view that what hadouts will be available to the maximizer on the next step.

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

Support your answer with examples of a few trees. Simulate the algorithm on the given graph below. Only two leaf nodes have been evaluated so far.

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

Is best first search always the best strategy? The static evaluation scores for each leaf node are written under it. Support your answer with an example tree.

They never consider that their might be more than one solution to the problem and the solution that they have ignored might be the optimal one. Will it always guarantee the best solution? The values on the nodes shown in yellow are the underestimates of the distance hadouts a specific node from G.

At last from H we find L as the best. Nandouts simple idea of branch and bound is the following: Among these, D the child of S is the best option. S is the initial state and D is the goal state. But in reality, exploring the entire search space is never feasible and at times is not even possible, for instance, if we just consider the tree corresponding to a game of chess we will learn about game trees laterthe effective branching factor is 16 and the effective depth is Use your suggested solutions to the above mention problems if any of ccs607 are encountered.

We will handoyts this improvement with an example.

### Artificial Intelligence – CS VU Lecture Handouts

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. To clarify the concept of adversarial search let us discuss a procedure called the minimax procedure. Both have their advantages and disadvantages.

Next we visit E, then we visit B the child of E, we handouys the sub-tree below B. There is no need to look at any other paths to or from Expanded f Never Expanded In the diagram you can see that we can reach node D directly from S with a ds607 of 3 and via S A D with a cost of 6 hence we will never expand the path with the larger cost of reaching the same node. Suggest solutions to the commonly encountered problems that are local maxima, plateau problem and ridge problem.

## CS607 Handouts

This approach is analogous to the brute force method and is also called the British museum procedure. The maximizer wishes to maximize the score so apparently 7 being the maximum score, the maximizer should go to Handputs and then to G. We have shown the sequence of steps in the diagrams below. The values on the links are the distances between the cities.

The player hoping for positive numbers is called maximizing player or maximizer. Try to model the problem in a graphical representation. We then move to F as that is the best option at this point with a value 7. We visit F and finally we reach G as shown in the subsequent diagrams.