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Problem formulation<br />Suppose that the agent's sensors give it enough information to tell exactly which state it is in (i.e., the world is accessible);<br /> Suppose that it knows exactly what each of its actions does. <br />Then it can calculate exactly which state it will be in after any sequence of actions.<br />
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Types of problems<br />When the environment is completely accessible and the agent can calculate its state after any sequence of action, we call it a single-state problem.<br />When the world is not fully accessible, the agent must reason about sets of states that it might get to, rather than single states. We call this a multiple-state problem.<br />
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Components of a Well defined problems and solutions<br />Data type<br />Components:<br />A Problem <br />An Operator <br />The Goal Test function <br />A Path Cost function<br />
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Measuring problem solving Performance<br /> The effectiveness of a search technique can be measured in at least three ways.1) Does it find a solution?2) Is it a good solution/low cost ?3) What is the search cost associated with the time and memory required to find a solution?<br />
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Problem formulation for 8-Queens Problem<br />Goal test: 8 queens on board: placing 8 queens on chess board so that no queen attacks each other.Path cost: zero.States: Any arrangement of 0 to 8 queens on board.Operators: Add a queen to any square.Route finding: It is used in a variety of applications, such as routing in computer networks, automated travel advisory systems, and airline travel planning systems. <br />
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Searching for solutions<br />Solution to an AI problems involves performing an action to go to one proper state among possible numerous possible states of agent.<br />Thus the processes of finding solution can be boiled down to searching of that best state among all the possible states.<br />
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Data structures for search trees<br />Data type node<br />Components: STATE, PARENT-NODE, OPERATOR, DEPTH, PATH-COST<br />The State in the state space to which the node corresponds.<br />The Node in the search tree that generated this node.<br />The Operator that was applied to generate the node.<br />The Number of nodes on the path from the root to this node (the depth of the node).<br />The path cost of the path from the initial state to the node.<br />
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Optimality of search algorithm's is based on following factors<br />Completeness: is the strategy guaranteed to find a solution when there is one?<br />Time complexity: how long does it take to find a solution?<br />Space complexity: how much memory does it need to perform the search?<br />Optimality: does the strategy find the highest-quality solution when there are several different solutions?<br />
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What is Breadth-first search?<br />One simple search strategy is a breadth-first search. <br />In this strategy, the root node is expanded first, then all the nodes generated by the root node are expanded next, and then their successors, and so on.<br />
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What is Uniform cost search?<br />Breadth-first search finds the shallowest goal state, but this may not always be the least-cost solution for a general path cost function. <br />Uniform cost search modifies the breadth-first strategy by always expanding the lowest-cost node on the fringe (as measured by the path cost g(n)), rather than the lowest-depth node.<br />
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What is Depth-first search?<br />Depth-first search always expands one of the nodes at the deepest level of the tree. <br />Only when the search hits a dead end (a non goal node with no expansion) does the search go back and expand nodes at shallower levels.<br />
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What is Depth-limited search?<br />Its basically similar to depth first search with following modification.<br />Depth-limited search avoids the pitfalls of depth-first search by imposing a cutoff on the maximum depth of a path.<br />
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What is Iterative deepening search?<br />Iterative deepening search is a strategy that sidesteps the issue of choosing the best depth limit by trying all possible depth limits:<br /> first depth 0, then depth 1, then depth 2, and so on.<br />
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What is Bidirectional search?<br />The idea behind bidirectional search is to simultaneously search both forward from the initial state and backward from the goal, and stop when the two searches meet in the middle .<br />
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