Lecture 3-Searching

# Lecture 3-Searching - Chapter 3 Searching Methodology...

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Chapter 3 : Searching Methodology Chapter 3 - Searching Methodology 1

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Recall KR Chapter 3 - Searching Methodology 2 Knowledge Representation Analysis Representation Coding Representation Inference Frames Production rules Semantic networks Decision tables Decision trees
1. What is Searching in AI? 2. What to Consider First? 3. Types of Searching Techniques 4. Un informed Search (Blind ) Breadth-First Search (BFS) Uniform Cost Search (UCS) Depth-First Search (DFS) Depth Limited Search (DLS) Iterative Deepening Search Bi-directional Search Chapter 3 - Searching Methodology 3 5. Informed Search (Heuristic ) Hill Climbing (HC) Best First Search (Best-S) Beam Search The A* Algorithm (A-Star) Learning Outcomes

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What is Searching in AI? In computer science , a search algorithm, is an algorithm that takes a problem as input and returns a solution to the problem, usually after evaluating a number of possible solutions. Chapter 3 - Searching Methodology 4
What is Searching in AI? Chapter 3 - Searching Methodology 5

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Branching Factors for Some Problems Chapter 3 - Searching Methodology 6 b = branching factor. The eight puzzle has a branching factor of 2.13, so a search tree at depth 20 has about 3.7 million nodes. (note that there only 181,400 different states). Rubik’s cube has a branching factor of 13.34. There are 901,083,404,981,813,616 different states. The average depth of a solution is about 18. The best time for solving the cube in an official championship was 17.04 sec, achieved by Robert Pergl in the 1983 Czechoslovakian Championship. In 1997 the best AI computer programs took weeks (See Korf, UCLA). Chess has a branching factor of about 35, there are about 10 120 states (there are about 10 79 electrons in the universe). successor 2 1 3 4 7 6 5 8
14 Jan 2004 CS 3243 - Blind Search 7 Search strategies A search strategy is defined by picking the order of node expansion Strategies are evaluated along the following dimensions: completeness : does it always find a solution if one exists? time complexity : number of nodes generated space complexity : maximum number of nodes in memory optimality : does it always find a least-cost solution? Time and space complexity are measured in terms of b: maximum branching factor of the search tree d: depth of the least-cost solution m : maximum depth of the state space (may be ∞)

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What to Consider First? Chapter 3 - Searching Methodology 8 A B C E D H I J K L M N O G F We are going to consider different techniques to search the problem space, we need to consider what criteria we will use to compare them. 1. Completeness : Is the technique guaranteed to find an answer (if there is one). 2. Optimality : Is the technique guaranteed to find the best answer (if there is more than one). (operators can have different costs) 3. Time Complexity : How long does it take to find a solution.
• Winter '14
• STUDENT
• Depth-first search, Search algorithms, Search algorithm, Hill climbing, Searching Methodology

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