'Search algorithm' diaporamas de présentation

Chapter 05 Constraint Satisfaction Problems

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Intro to Knowledge Based Systems

Intro to Knowledge Based Systems. CS 343 Elaine Rich. The Key Issues. What knowledge do we need? How can it be represented? How can we reason with it? Where does it come from? Our sandbox: Question answering. Stating the Facts. Painter(Leonardo) Composer(Palistrina) Country(Italy)

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Graph Theory

Graph Theory. Love Ekenberg. Graphs. A graph is a structure (V,E), where V is a finite set called the vertex set and E the set of edges. E consists of 2-tuples of elements in V. Example: V = {a,b,c,d,e}, E = {(a,b),(b,c),(d,e)}.

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Chapter 6

Chapter 6. Prune-and-search. A simple example: Binary search. sorted sequence : (search 9) 1 4 5 7 9 10 12 15 step 1 ? step 2 ? step 3 ? After each comparison, a half of the data set are pruned away .

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Informed search algorithms

Informed search algorithms. Chapter 4 Slides derived in part from www.cs.berkeley.edu/~russell/slides/chapter04a.pdf, converted to powerpoint by Min-Yen Kan, National University of Singapore,

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Target Tracking in Sensor Networks

17 th Oct 2005. Presented By: Arpit Sheth. Target Tracking in Sensor Networks. Introduction. One of the most important applications of sensors is target tracking. Each node can sense in multiple modalities such as acoustic, seismic and infrared.

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Suffix Trees and Suffix Arrays

Suffix Trees and Suffix Arrays. Some problems. Given a pattern P = P[1..m], find all occurrences of P in a text S = S[1..n] Another problem: Given two strings S 1 [1..n 1 ] and S 2 [1..n 2 ] find their longest common substring.

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Suffix Trees and Suffix Arrays

Suffix Trees and Suffix Arrays. Some problems. Given a pattern P = P[1..m], find all occurrences of P in a text S = S[1..n] Another problem: Given two strings S 1 [1..n 1 ] and S 2 [1..n 2 ] find their longest common substring.

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Uninformed Search Computer Science cpsc322, Lecture 5 (Textbook Chpt 3.5) Sept, 14, 2012

Uninformed Search Computer Science cpsc322, Lecture 5 (Textbook Chpt 3.5) Sept, 14, 2012. Recap. Search is a key computational mechanism in many AI agents We will study the basic principles of search on the simple deterministic planning agent model Generic search approach :

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Swarm Intelligence An Introduction

Swarm Intelligence An Introduction. Applied Artificial Intelligence (Third Semester, ME in CSN/CIE). What is Swarm?. A loosely structured collection of interacting agents Agents: Individuals that belong to a group (but are not necessarily identical)

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Important SEO Terms You Should know

Search Engine Optimization, often referred to as SEO, plays the most important role in the shaping up of any blog or website today.

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Potential Phytochemicals for HIV Reverse Transcriptase Inhibition : A Docking Study

Potential Phytochemicals for HIV Reverse Transcriptase Inhibition : A Docking Study. Abhik Seal 1 & Riju Aykkal 2 1 Doeacc Centre Kolkata ( Jadavpur University Campus), Kolkata, West Bengal. abhik1368@gmail.com. 2 Computer Programmer, Cantonment Board, Kannur , Kerala . . INTRODUCTION.

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Searching and Sorting

Searching and Sorting. Topics Sequential Search on an Unordered File Sequential Search on an Ordered File Binary Search Bubble Sort Insertion Sort. Common Problems. There are some very common problems that we use computers to solve:

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Overview of Artificial Intelligence

Overview of Artificial Intelligence. Thomas R. Ioerger Associate Professor Department of Computer Science Texas A&M University. What is AI?. Real applications, not science fiction

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Parallel Search Algorithm

Parallel Search Algorithm. By: Mehdi Amini. Spring 2012. Isfahan University Of Technology. OutLine :. Type Of Search Algorithms Searching in Artificial Intelligence Sequential Depth First Search Sequential Best First Search Parallel Depth First Search Parallel Best First Search

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Efficiency of Algorithms

Efficiency of Algorithms. February 9th. Today. Data cleanup algorithms Copy-over Shuffle-left Converging pointers Algorithm efficiency Efficiency of data cleanup algorithms Reading: Chapter 3. Comparing Algorithms. Algorithm Design Correctness Efficiency

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Backtracking Search Vs. Variable Elimination for Propositional Satisfiability

Backtracking Search Vs. Variable Elimination for Propositional Satisfiability. Willis Lemasters Grant Conklin. Backtracking Search:. Searching a tree recursively one branch at a time, abandoning any branch which does not satisfy the search constraints. Variable Elimination:.

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C&O 355 Lecture 5

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Design Optimization Approach for FML Wing Structure

Design Optimization Approach for FML Wing Structure. PhD Candidate: Ilhan ?en Department: ASM Section: Structural Integrity & Composites Supervisor: R. C. Alderliesten Promoter: R. Benedictus Start date: 1-6-2011 Cooperation: Constellium. Optimization Procedure

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