function to evaluate the remaining cost to get to the goal from the A* search algorithm. Iterative Deepening A-star (IDA*) CSC 171 – Introduction to AI 3 Description ● ● ● ● IDA* ‚ a search algorithm‚ a combination of the A* algorithm and the DFS algorithm.[1] Invented by Korf in 1985.[1] The idea is that successive iterations correspond not to increasing depth of search‚ but rather to increasing values of the total cost of a path. [1] The cost of a node is (using A* terms) f=g+h g = cost incurred
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[pic] A Study of Factors Driving Shareholders’ Value and Influencing Sensex Fluctuation In India Executive Summary The objective of this project is to analyze the most important factors which drive shareholders‚ value. Shareholders’ value here refers to the MVA (market value added) which means the additional value which shareholders are earning on their invested money. The performance of a company matters a lot in creating a positive image of that company in front of its stakeholders. Moreover
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LABORATORY FOR PRODUCTION MEASUREMENT Faculty of Mechanical Engineering Smetanova 17‚ 2000 Maribor‚ Slovenia SOP 6 CALIBRATION OF VERNIER CALLIPER GAUGES Issue date 11.7.2003 Issue No. E-1 Approved by Bojan Ačko‚ Ph.D. Changes made AMENDMENT REMOVED No. Section Date ADDED Page Issue No. Section Present issue E-1 is equal to the issue No. 3 dated September 1998 Page Issue No. CONTENTS 1 1.1 INTRODUCTION General aspects 4 4
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Basic Language Training: Part One List of contents BLT 1 Introduction‚ Aims and Objectives Syllabus Lesson plan Lesson 1 Lesson 2 Lesson 3 Lesson 4 Review 1 Lesson 5 Lesson 6 Lesson 7 Lesson 8 Review 2 Lesson 9 Lesson 10 Lesson 11 Lesson 12 Review 3 Lesson 13 Lesson 14 Lesson 15 Review 4 2nd day dialogues: A’s version 2nd day dialogues: B’s version Listening texts: Lesson 1 – 15 Appendix: Verb list Combined vocabulary list List of post positions Sentence structure charts
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Multiple Regression Analysis 16 3. Multiple Regression Analysis The concepts and principles developed in dealing with simple linear regression (i.e. one explanatory variable) may be extended to deal with several explanatory variables. We begin with an example of two explanatory variables‚ both of which are continuous. The regression equation in such a case becomes: Y = α + β1x1 + β2 x2 It is customary to replace α with β 0‚ and so all future regression equations would be written as
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Comparative Analysis for Maruti-Suzuki Ertiga Comparative Analysis for Maruti-Suzuki Ertiga Submitted by: Group 6 (Section C PGDM 1st yr) Anisha Gupta 152/2013 Hemant Tejwani 153/2013 Anmolika Dhillon 154/2013 Ravjot Singh 155/2013 Gaurav Bhudiraja 156/2013 Nitish Gagneja 157/2013 Submitted to: Prof. Shikha Singh
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Acknowledgment All the thanks to Almighty Allah‚ Who bestowed us with courage & ability to achieve this opportunity. We would like to thank Ms. Zainab Rehman (Course Instructor) for her continuous support and guidance she has rendered for the successful completion of this project. We have collected the data for our research through survey method and questionnaires and we are in no confusion for saying that this activity has enhanced our knowledge about the Research work. It is the result
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Graphs 1 Introduction We have studied one non-linear data structure so far i.e Trees. A graph is another non-linear data structure that is widely used to solve many real-life computing problems. For example‚ we need to use a graph to find out whether two places on a road-map are connected and what is the shortest distance between them. Graphs are used in simulating electrical circuits to find out current flows and voltage drops at various points in the circuit. Graphs are widely used in telephone
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Identifying the Need for Instruction on Media and Information Literacy for Teenagers Rolando Tan Ma. Consuelo C. Maaghop Glenah A. Furio Jun Roa Jubal Arbolario Josiah Mari C. Tiburcio Strategy: - Interview with guidance counselors‚ class advisers & discipline officer on these teen age problematic cases - Interview on parents of problematic teens - Likert Scale on teenagers on the different activities they are engaged into (parties‚ camping‚ gimmicks‚ spending time in the
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| | | | Method | df | Value | Probability | | | | | | | | | | | t-test | 310 | -4.033067 | 0.0001 | Satterthwaite-Welch t-test* | 306.5077 | -4.033067 | 0.0001 | Anova F-test | (1‚ 310) | 16.26563 | 0.0001 | Welch F-test* | (1‚ 306.508) | 16.26563 | 0.0001 | | | | | | | | | | | *Test allows for unequal cell variances | | | | | | | Analysis of Variance | | | | | | | | | | | | | Source of Variation | df | Sum of Sq. | Mean Sq.
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