I.ITERATIVE DEEPENING DEPTH FIRST SEARCH(ID-DFS) II.INFORMED SEARCH IN ARTIFICIAL INTELLIGENCE III.HEURISTIC FUNCTION IN AI
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The document covers various search techniques in artificial intelligence, including iterative deepening depth-first search (ID-DFS), informed search, and heuristic functions. ID-DFS combines the benefits of depth-first and breadth-first search while ensuring completeness and optimality, although it has memory limitations. Informed search uses additional knowledge to efficiently find solutions, employing heuristics to guide the search process, with methods like Euclidean and Manhattan distance for estimating heuristic values.
I.ITERATIVE DEEPENING DEPTH FIRST SEARCH(ID-DFS) II.INFORMED SEARCH IN ARTIFICIAL INTELLIGENCE III.HEURISTIC FUNCTION IN AI
- 1. Topic To Be Covered: I. ITERATIVE DEEPENING DEPTH FIRST SEARCH(ID-DFS) II.INFORMED SEARCH IN ARTIFICIAL INTELLIGENCE III. HEURISTIC FUNCTION IN AI Jagdamba Education Society's SND College of Engineering & Research Centre Department of Computer Engineering SUBJECT: Artificial Intelligence & Robotics Lecture No-08 Prof.Dhakane Vikas N
- 2. ITERATIVE DEEPENING DEPTH FIRST SEARCH(ID-DFS This is type of Uninformed search technique also called as blind search. This algo. Works on only present value. This algorithm is combination of DFS & BFS It uses STACK (LIFO) to perform search operations Utilizes Deepest & shallowest Node
- 3. ITERATIVE DEEPENING DEPTH FIRST SEARCH(ID-DFS Best depth limit is found out by gradually increasing depth limit on each iterations. Initially depth limit is =0 On each iteration depth increased by exactly one. S->Initial Node Or Source Node G->Goal Node Or Target Node
- 4. ITERATIVE DEEPENING DEPTH FIRST SEARCH(ID-DFS Advantages of ID-DFS: Combine the advantage of both DFS and BF It is Complete & Optimal Will not go in infinite loop Disadvantages of ID-DFS: Regressive Recursion is required May required more memory
- 5. Types of search in ai II. Informed Search In AI It is search with information. It is greedy search method Use knowledge to find steps to solution. Less Complexity(Time, Space) Quick Solution Know about Start state and Goal state & Also know how to reach. informed search algorithm contains an array of knowledge such as how far we are from the goal, path cost, how to reach to goal node, etc. This knowledge help agents to explore less to the search space and find more efficiently the goal node. The informed search algorithm is more useful for large search space. Informed search algorithm uses the idea of heuristic, so it is also called Heuristic search.
- 6. HEURISTIC FUNCTION in ai Heuristic is a function which is used in Informed Search, and it finds the most promising path. It takes the current state of the agent as its input and produces the estimation of how close agent is from the goal. Heuristic value or Function act as Guide in IS. Heuristic function estimates how close a state is to the goal. It is represented by h(n), and it calculates the cost of an optimal path between the pair of states. The heuristic method, however, might not always give the best solution, but it guaranteed to find a good solution in reasonable time. This technique always use to find solution quickly.
- 7. HEURISTIC FUNCTION in ai Heuristic is a function which is used in Informed Search, and it finds the most promising path. It takes the current state of the agent as its input and produces the estimation of how close agent is from the goal. Heuristic function estimates how close a state is to the goal. It is represented by h(n), and it calculates the cost of an optimal path between the pair of states. The heuristic method, however, might not always give the best solution, but it guaranteed to find a good solution in reasonable time.
- 8. HEURISTIC FUNCTION in ai Different Method Used To Estimate Heuristic Value --------------------------------------------------------------------------- I. Euclidian Distance (Straight Line Distance Method) Euclidean distance is the distance between two points in Euclidean space. Euclidean space was originally devised by the Greek mathematician Euclid around 300 B.C.E. to study the relationships between angles and distances.
- 9. HEURISTIC FUNCTION in ai Different Method Used To Estimate Heuristic Value II. Manhattan Distance In case of 8-Puzzle problem Manhattan distances are nothing but Number of moves need to be made by AI agent so that it can reach to its goal state.
- 10. HEURISTIC FUNCTION in ai Different Method Used To Estimate Heuristic Value ------------------------------- III. No.of Misplaced Tiles
- 11. BEST FIRST SEARCH(BFS) This is informed search technique also called as HEURISTIC search. This algo. Works using heuristic value. This algorithm uses evaluation function to decide which adjacent node is most promising and then explore. Priority queue is used to store cost of function. Space &Time Complexity of BFS is also O(V+E) whereV is vertices and E is edges. Also Written as:-O(b) ^d Where, b->Branching factor d->depth
- 12. BEST FIRST SEARCH(BFS) Algorithm Priority queue ‘PQ’ containing initial states Loop If PQ=Empty Return Fail Else NODE<-Remove_First(PQ) If NODE=GOAL Return path from initial state to NODE ELSE Generate all successor of NODE and insert newly generated NODE into ‘PQ’ according to cost value. END LOOP
- 13. BEST FIRST SEARCH(BFS) Advantages of BFS: Memory efficient as compared with DFS & BFS It is Complete Disadvantages of BFS: It gives good solution but not optimal solution. In worst case it may behave like unguided DFS