Problem Solving and Searching IT Elect 104 (Chapter 3) Some text and images in these slides were drawn from Russel & Norvig’s published material Problem Solving Agent Function Problem Solving Agent * Agent finds an action sequence to achieve a goal * Requires problem formulation * Determine goal * Formulate problem based on goal * Searches for an action sequence that solves the problem * Actions are then carried out‚ ignoring percepts during that period
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“e” using DFS with tree-search algorithm‚ and then with graph-search algorithm. You have to show: 1. The search tree. 2. The order in which nodes will be visited. 3. The fringe at each step. 4. The closed set (in case of graph-search only). 5. The solution path. 6. The fringe (and closed set) after the search is terminated. Note: expand nodes in alphabetical order when you have more than one candidate for expansion. Using Graph search algorithm: Order of node visited : a
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// FREQUENT SUBTREE MINING ALGORITHM... #include #include #include #include #include #include using namespace std; FILE *fp; int no_of_nodes=0‚ string_ctr=0‚ vect_ctr=0‚ vect_ctr1=0‚pos_ctr=0‚*pos; struct MyNode { string name; vector children; }*myroot‚ *myroot1‚ **tree_pattern‚ **subtree_pattern; //FUNCTION PROTOTYPES DECLARATION ... static void print_element_names(xmlNode *); static MyNode* preprocess(xmlNode *‚MyNode *‚ int); int printMyNode(MyNode *); void
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regression seeks to optimise the parameters for a pre-specified model structure‚ with symbolic regression‚ the model structure and parameters are determined simultaneously. Similarly‚ the evolution of control algorithms‚ scheduling programs‚ structural design and signal processing algorithms can be viewed as structural optimisation problems suitable for GP. Abstract The aim of this paper is to provide an introduction to the rapidly developing field of genetic programming (GP). Particular emphasis
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Fundamentals of programming with algorithms and logic Benjamin Sigala April 5‚ 2010 Computer programs are a form of language written out processes that carry out different tasks. Like human society there are different languages and different ways of speaking that language‚ the same goes for software design. There are three basic types
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Stremmer Algorithm Overview The Porter Stemmer is a conflation Stemmer developed by Martin Porter at the University of Cambridge in 1980. The stemmer is a context sensitive suffix removal algorithm. It is the most widely used of all the stemmers and implementations in many languages are available. This native functor creates a module that exports a function which performs stemming by means of the Porter stemming algorithm. Quoting Martin Porter himself: The Porter stemming algorithm (or ’Porter
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TECHNOLOGY BHUBANESWAR Digital Signal Processing Lab Report 6 Submitted by Name Allam Levi Ratnakar B Suresh Roll Number 08EEB025 08EEB026 Problem: Design a model for the plant h (z) =0.2600+0.9300z¯¹+0.2600z¯² using direct modeling (Adaptive Algorithm LMS/RMS). The channel is associated with the following functions. where p(k) is the output of each of linear part of the channels Theory: The aim of this experiment is to create a model for a plant with given parameters using direct modeling
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transportation related analyses. Choosing an adequate algorithm from the numerous algorithms reported in the literature is a critical step in many applications involving real road networks. In a recent study‚ a set of two shortest path algorithms that run fastest on real road networks has been identified. These two algorithms are: 1) the A* algorithm‚ 2) the Dijkstra’s algorithm. As a sequel to that study‚ this paper reviews and summarizes these two algorithms‚ and demonstrates the data structures and
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STIN1013 INTRODUCTION TO ARTIFICIAL INTELLIGENCE Semester February Session 2012/2013 (A122) ASSIGNMENT_4 PROBLEM SOLVING AND SEARCHING Total marks: Deadline: Terms and Condition: 10 27th May 2013 1. Submission This is a group assignment. Each group must submit only ONE (1) answer script. Answers must be in computer-typed form; it only needs to be stapled on its top left without any plastic cover on it. 2. Assessment Assessment will be based on the submitted script. All members of the group will
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Linear Programming History of linear programming goes back as far as 1940s. Main motivation for the need of linear programming goes back to the war time when they needed ways to solve many complex planning problems. The simplex method which is used to solve linear programming was developed by George B. Dantzig‚ in 1947. Dantzig‚ was one in who did a lot of work on linear programming‚ he was reconzied by several honours. Dantzig’s discovery was through his personal contribution‚ during WWII when Dantzig
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