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# Heuristic Search Techniques Unit -II.ppt

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# Heuristic Search Techniques Unit -II.ppt

AI, Heuristic algorithms, generate and test,
Hill climbing Algorithm
A* algorithm

AI, Heuristic algorithms, generate and test,
Hill climbing Algorithm
A* algorithm

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### Heuristic Search Techniques Unit -II.ppt

1. 1. HEURISTIC SEARCH TECHNIQUES Dr.M.Karthika Assistant Professor/ IT MTNC,Madurai.
2. 2. WHAT IS A HEURISTIC? 2/7/2023 2
3. 3. WHAT IS A HEURISTIC SEARCH? • A Heuristic is a technique to solve a problem faster than classic methods, or to find an approximate solution when classic methods cannot. • A Heuristic (or a heuristic function) takes a look at search algorithms. At each branching step, it evaluates the available information and makes a decision on which branch to follow. • It does so by ranking alternatives. The Heuristic is any device that is often effective but will not guarantee work in every case. • This is a kind of a shortcut as we often trade one of optimality, completeness, accuracy, or precision for speed. 2/7/2023 3
4. 4. 2/7/2023 4
5. 5. WHY DO WE NEED HEURISTICS? • To produce a solution , in a reasonable amount of time. It doesn’t have to be the best- an approximate solution will do since this is fast enough. • Reduce the polynomial number for most problems that are exponential. And in situations where we can’t find known algorithms. • Heuristic Techniques may be weak methods because they are vulnerable to combinatorial explosion. 2/7/2023 5
6. 6. • Other names for these are Blind Search, Uninformed Search, and Blind Control Strategy. • These aren’t always possible since they demand much time or memory. • They search the entire state space for a solution and use an arbitrary ordering of operations. • Examples of these are Breadth First Search (BFS) and Depth First Search (DFS). DIRECT HEURISTIC SEARCH TECHNIQUES IN AI 2/7/2023 6
7. 7. WEAK HEURISTIC SEARCH TECHNIQUES IN AI • Other names for these are Informed Search, Heuristic Search, and Heuristic Control Strategy. • These are effective if applied correctly to the right types of tasks and usually demand domain-specific information. • Examples are Best First Search (BFS) and A*. • Best-First Search • A* Search • Bidirectional Search • Tabu Search • Beam Search • Simulated Annealing • Hill Climbing • Constraint Satisfaction Problems 2/7/2023 7
8. 8. HEURISTIC ALGORITHMS 2/7/2023 8
9. 9. HILL CLIMBING – ANOTHER EXAMPLE • Problem: You have just arrived in Washington, D.C. You’re in your car, trying to get downtown to the Washington Monument. 2/7/2023 9
10. 10. FEATURES OF HILL CLIMBING IN AI • Generate and Test variant: Hill Climbing is the variant of Generate and Test method. The Generate and Test method produce feedback which helps to decide which direction to move in the search space. • Greedy approach: Hill-climbing algorithm search moves in the direction which optimizes the cost. • No backtracking: It does not backtrack the search space, as it does not remember the previous states. 2/7/2023 10
11. 11. PROBLEMS WITH HILL CLIMBING IN AI Three issues Addressed • Local Maximum- All neighboring states have values worse than the current. The greedy approach means we won’t be moving to a worse state. This terminates the process even though there may have been a better solution. As a workaround, we use backtracking. • Plateau- All neighbors to it have the same value. This makes it impossible to choose a direction. To avoid this, we randomly make a big jump. • Ridge- At a ridge, movement in all possible directions is downward. This makes it look like a peak and terminates the process. To avoid this, we may use two or more rules before testing. 2/7/2023 11
12. 12. STATE-SPACE DIAGRAM ANALYSIS 2/7/2023 12
13. 13. GENERATE AND TEST SEARCH • Is a heuristic search technique based on Depth First Search with Backtracking which guarantees to find a solution if done systematically and there exists a solution. • In this technique, all the solutions are generated and tested for the best solution. • It ensures that the best solution is checked against all possible generated solutions. • It is also known as British Museum Search Algorithm as it’s like looking for an exhibit at random or finding an object in the British Museum by wandering randomly. 2/7/2023 13
14. 14. GENERATE AND TEST SEARCH Step:1 Generate a possible solution. For example, generating a particular point in the problem space or generating a path for a start state. Step:2Test to see if this is a actual solution by comparing the chosen point or the endpoint of the chosen path to the set of acceptable goal states Step:3 If a solution is found, quit. Otherwise go to Step 1 2/7/2023 14
15. 15. TYPES OF HILL CLIMBING IN AI 2/7/2023 15
16. 16. SIMPLE HILL CLIMBING • Examines one neighboring node at a time and selects the first one that optimizes the current cost to be the next node. • Algorithm: 1. Evaluate initial state- if goal state, stop and return success. Else, make initial state current. 2. Loop until the solution reached or until no new operators left to apply to current state: a. Select new operator to apply to the current producing new state. b. Evaluate new state: • If a goal state, stop and return success. • If better than the current state, make it current state, proceed. • Even if not better than the current state, continue until the solution reached. 3. Exit. 2/7/2023 16
17. 17. FEATURES: • Less time consuming • Less optimal solution and the solution is not guaranteed 2/7/2023 17
18. 18. STEEPEST-ASCENT HILL CLIMBING: • The steepest-Ascent algorithm is a variation of simple hill climbing algorithm. This algorithm examines all the neighboring nodes of the current state and selects one neighbor node which is closest to the goal state. This algorithm consumes more time as it searches for multiple neighbors 2/7/2023 18
19. 19. ALGORITHM FOR STEEPEST-ASCENT HILL CLIMBING • Step 1: Evaluate the initial state, if it is goal state then return success and stop, else make current state as initial state. • Step 2: Loop until a solution is found or the current state does not change. • Let SUCC be a state such that any successor of the current state will be better than it. • For each operator that applies to the current state: • Apply the new operator and generate a new state. • Evaluate the new state. • If it is goal state, then return it and quit, else compare it to the SUCC. • If it is better than SUCC, then set new state as SUCC. • If the SUCC is better than the current state, then set current state to SUCC. • Step 3: Exit. 2/7/2023 19
20. 20. ANNEALING • Annealing is a thermal process for obtaining low energy states of a solid in a heat bath. • The process contains two steps: • Increase the temperature of the heat bath to a maximum value at which the solid melts. • Decrease carefully the temperature of the heat bath until the particles arrange themselves in the ground state of the solid. Ground state is a minimum energy state of the solid. • The ground state of the solid is obtained only if the maximum temperature is high enough and the cooling is done slowly. 2/7/2023 20
21. 21. SIMULATED ANNEALING • Simulated annealing maintains a current assignment of values to variables. • At each step, it picks a variable at random, then picks a value at random. If assigning that value to the variable is an improvement or does not increase the number of conflicts, the algorithm accepts the assignment and there is a new current assignment. • Otherwise, it accepts the assignment with some probability, depending on the temperature and how much worse it is than the current assignment. If the change is not accepted, the current assignment is unchanged. 2/7/2023 21
22. 22. • To control how many worsening steps are accepted, there is a positive real-valued temperature T. • Suppose A is the current assignment of a value to each variable. Suppose that h(A) is the evaluation of assignment A to be minimized. • For solving constraints, h is typically the number of conflicts. Simulated annealing selects a neighbor at random, which gives a new assignment A'. If h(A') ≤ h(A), it accepts the assignment and A' becomes the new assignment. Otherwise, the assignment is only accepted randomly with probability • e(h(A)-h(A'))/T. • Thus, if h(A') is close to h(A), the assignment is more likely to be accepted. If the temperature is high, the exponent will be close to zero, and so the probability will be close to 1. As the temperature approaches zero, the exponent approaches -∞, and the probability approaches zero. 2/7/2023 22
23. 23. BEST FIRST SEARCH • OR Graphs • The A* Algorithm 2/7/2023 23
24. 24. OR GRAPHS • BFS uses the concept of a Priority queue and heuristic search. • To search the graph space, the BFS method uses two lists for tracking the traversal. • An ‘Open’ list that keeps track of the current ‘immediate’ nodes available for traversal and a ‘CLOSED’ list that keeps track of the nodes already traversed. 2/7/2023 24
25. 25. BEST FIRST SEARCH ALGORITHM • Create 2 empty lists: OPEN and CLOSED • Start from the initial node (say N) and put it in the ‘ordered’ OPEN list • Repeat the next steps until the GOAL node is reached • If the OPEN list is empty, then EXIT the loop returning ‘False’ • Select the first/top node (say N) in the OPEN list and move it to the CLOSED list. Also, capture the information of the parent node • If N is a GOAL node, then move the node to the Closed list and exit the loop returning ‘True’. The solution can be found by backtracking the path • If N is not the GOAL node, expand node N to generate the ‘immediate’ next nodes linked to node N and add all those to the OPEN list • Reorder the nodes in the OPEN list in ascending order according to an evaluation function f(n) 2/7/2023 25
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27. 27. ADVANTAGES AND DISADVANTAGES OF BEST FIRST SEARCH • Advantages: 1. Can switch between BFS and DFS, thus gaining the advantages of both. 2. More efficient when compared to DFS. • Disadvantages: 1. Chances of getting stuck in a loop are higher. 2/7/2023 27
28. 28. A* SEARCH ALGORITHM A* search is the most commonly known form of best-first search. It uses heuristic function h(n), and cost to reach the node n from the start state g(n). It has combined features of UCS and greedy best-first search, by which it solve the problem efficiently. A* search algorithm finds the shortest path through the search space using the heuristic function. This search algorithm expands less search tree and provides optimal result faster. A* algorithm is similar to UCS except that it uses g(n)+h(n) instead of g(n). In A* search algorithm, we use search heuristic as well as the cost to reach the node. Hence we can combine both costs as following, and this sum is called as a fitness number. Example 2/7/2023 28
29. 29. ALGORITHM Step1: Place the starting node in the OPEN list. Step 2: Check if the OPEN list is empty or not, if the list is empty then return failure and stops. Step 3: Select the node from the OPEN list which has the smallest value of evaluation function (g+h), if node n is goal node then return success and stop, otherwise Step 4: Expand node n and generate all of its successors, and put n into the closed list. For each successor n', check whether n' is already in the OPEN or CLOSED list, if not then compute evaluation function for n' and place into Open list. Step 5: Else if node n' is already in OPEN and CLOSED, then it should be attached to the back pointer which reflects the lowest g(n') value. Step 6: Return to Step 2. 2/7/2023 29
30. 30. Advantages: • A* search algorithm is the best algorithm than other search algorithms. • A* search algorithm is optimal and complete. • This algorithm can solve very complex problems. Disadvantages: • It does not always produce the shortest path as it mostly based on heuristics and approximation. • A* search algorithm has some complexity issues. • The main drawback of A* is memory requirement as it keeps all generated nodes in the memory, so it is not practical for various large-scale problems. 2/7/2023 30
31. 31. THANK YOU 2/7/2023 31