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Problem-solving - having GOALS, image, image, image, image, image, image,…
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Solution. The equivalent search tree for the above graph is as follows. As BFS traverses the tree “shallowest node first”, it would always pick the shallower branch until it reaches the solution
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problem solving is a part of artificial intelligence that encompasses a number of techniques such as a tree, B-tree, heuristic algorithms to solve a problem.
formulating ensuring appropriate, using algorithms, and conducting root-cause analyses that identify reasonable solutions
Links denote actions: L = Left, R =Right, S = Suck.
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• Initial State: Wolf, goat, cabbage and farmer are on the north side of the river.
• Goal State: Wolf, goat, cabbage and farmer are on the south side of the river.
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Farmer can move one thing at a time across the river in the boat, or he can cross alone. But the goat cannot be left alone with the cabbage or with the wolf.
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Solution. The equivalent search tree for the above graph is as follows. As DFS traverses the tree “deepest node first”, it would always pick the deeper branch until it reaches the solution (or it runs out of nodes, and goes to the next branch). The traversal is shown in blue arrows.
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Solution. The equivalent search tree for the above graph is as follows. The cost of each node is the cumulative cost of reaching that node from the root.
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Time complexity: T(n) = O(n^C ^/ ^\varepsilon) , Space complexity: S(n) = O(n^C ^/ ^\varepsilon)
Starting from S, we can traverse to A(h=9) or D(h=5). We choose D, as it has the lower heuristic cost
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Advantage: Works well with informed search problems, with fewer steps to reach a goal.
Solution. Starting from S, the algorithm computes g(x) + h(x) for all nodes in the fringe at each step, choosing the node with the lowest sum.
Solution. We solve this question pretty much the same way we solved last question,
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. Game Playing: Algorithms analyze possible moves and their consequences, like the minimax algorithm with alpha-beta pruning in chess
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