AIMA_ch3 - Cooperating Intelligent Systems Uninformed...

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Cooperating Intelligent Systems Uninformed search Chapter 3, AIMA A goal based agent
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A ”problem” consists of An initial state, θ(029 A list of possible actions, α , for the agent A goal test (there can be many goal states) A path cost One way to solve this is to search for a path θ (029 → θ(129 → θ(229 → . .. → ( N ) such that  θ( N 29 is a goal state. This requires that the environment is  observable deterministic static  and  discrete .
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Example: 8-puzzle State: Specification of each of the eight tiles in the nine squares (the blank is in the remaining square). Initial state: Any state. Successor function (actions): Blank moves Left, Right, Up , or Down. Goal test: Check whether the goal state has been reached. Path cost: Each move costs 1. The path cost = the number of moves. 2 8 3 1 6 4 7 5 1 2 3 8 6 4 7 5 Start State Goal State
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Example: 8-puzzle State: Specification of each of the eight tiles in the nine squares (the blank is in the remaining square). Initial state: Any state. Successor function (actions): Blank moves Left, Right, Up , or Down. Goal test: Check whether the goal state has been reached. Path cost: Each move costs 1. The path cost = the number of moves. 2 8 3 1 6 4 7 5 1 2 3 8 6 4 7 5 Start State Goal State Examples: θ = {7, 2, 4, 5, 0, 6, 8, 3, 1} θ = {2, 8, 3, 1, 6, 4, 7, 0, 5}
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Example: 8-puzzle State: Specification of each of the eight tiles in the nine squares (the blank is in the remaining square). Initial state: Any state. Successor function (actions): Blank moves Left, Right, Up , or Down. Goal test: Check whether the goal state has been reached. Path cost: Each move costs 1. The path cost = the number of moves. 2 8 3 1 6 4 7 5 1 2 3 8 6 4 7 5 Start State Goal State
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Expanding 8-puzzle 2 8 3 1 6 4 7 5 2 8 3 1 6 4 7 5 2 8 3 1 6 4 7 5 2 8 3 1 6 4 7 5 Blank moves left Blank moves right Blank moves up θ = {2, 8, 3, 1, 6, 4, 7, 0, 5} θ = {2, 8, 3, 1, 6, 4, 0, 7, 5} θ = {2, 8, 3, 1, 0, 4, 7, 6, 5} θ = {2, 8, 3, 1, 6, 4, 7, 5, 0}
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Uninformed search Searching for the goal without knowing in which direction it is. Breadth-first Depth-first Iterative deepening (Depth and breadth refers to the search tree) We evaluate the algorithms by their: Completeness (do they explore all possibilities) Optimality (if it finds solution with minimum path cost) Time complexity (clock cycles) Space complexity (memory requirement)
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Breadth-first Breadth-first finds the solution that is closest (in the graph) to the start node (always expands the shallowest node). Keeps O(b d ) nodes in memory exponential memory requirement!
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AIMA_ch3 - Cooperating Intelligent Systems Uninformed...

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