Lec 6 - id A is a similar idea to iterative deepening a huge shift in AI However you may add a node each time you go down a level so that would lead to

Lec 6 - id A is a similar idea to iterative deepening a...

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id A* is a similar idea to iterative deepening - a huge shift in AI However, you may add a node each time you go down a level, so that would lead to a huge overhead, which is not the case for iterative deepening New method: Constraint Satisfaction It’s seen in the map coloring problem Another type is SAT, or satisfiability, which uses boolean values The idea is: assign a value of true or false to all x 1 … x n such that all constraints are satisfied. Procedure: Setting up: Assign values. Values V 1 … V n (each has d values) Children C 1 …C m Initial State: {} { V 2 * E 3 } it branches nd times. Each level down, you get (n-i) * d values, so it’s exponential If we make a constraint graph and it is a tree, we can search it in linear time. Constraint Satisfaction: 1) ordering variables 2) ordering values 3) early detection of failure 4) exploiting problem structure Example of ordering variables -most constrain ed variable: on a map, it’s the state with the most neighbors -most constrain ing variable

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• Spring '06
• Staff
• Economics, Constraint, Constraint satisfaction, Constraint satisfaction problem, Values V1… Vn

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