Computer Science Subject Topic

MEANS - ENDS ANALYSIS:- Most of the search strategies either reason forward of backward however, often a mixture o the two directions is appropriate. Such mixed strategy would make it possible to solve the major parts of problem first and solve the smaller problems the arise when combining them together. Such a technique is called "Means - Ends Analysis". The means -ends analysis process centers around finding the difference between current state and goal state. The problem space of means - ends analysis has an initial state and one or more goal state, a set of operate with a set of preconditions their application and difference functions that computes the difference between two state a(i) and s(j). A problem is solved using means - ends analysis by 1. Computing the current state s1 to a goal state s2 and computing their difference D12. 2. Satisfy the preconditions for some recommended operator op is selected, then to reduce the difference D12. 3. The operator OP ...

AO* Algorithm. The AO* ALGORITHM The problem reduction algorithm we just described is a simplification of an algorithm described in Martelli and Montanari, Martelli and Montanari and Nilson. Nilsson calls it the AO* algorithm , the name we assume. 1. Place the start node s on open. 2. Using the search tree constructed thus far, compute the most promising solution tree T 3. Select a node n that is both on open and a part of T. Remove n from open and place it on closed. 4. If n is a terminal goal node, label n as solved. If the solution of n results in any of n’s ancestors being solved, label all the ancestors as solved. If the start node s is solved, exit with success where T is the solution tree. Remove from open all nodes with a solved ancestor 5. If n is not a solvable node (operators cannot be applied), label n as unsolvable. If the start node is labeled as unsolvable, exit with failure. If any of n’s ancestors become unsolvable because n is, label them u...

A* Algorithm. A* algorithm: The best first search algorithm that was just presented is a simplification an algorithm called A* algorithm which was first presented by HART. Algorithm: Step 1: put the initial node on a list start Step 2: if (start is empty) or (start = goal) terminate search. Step 3: remove the first node from the start call this node a Step 4: if (a= goal) terminate search with success. Step 5: else if node has successors generate all of them estimate the fitness number of the successors by totaling the evaluation function value and the cost function value and sort the fitness number. Step 6: name the new list as start 1. Step 7: replace start with start 1. Step 8: go to step 2.