AN AGENT-BASED INTELLIGENT TUTORING SYSTEM FOR GRADE FOUR ENGLISH PUPILS OF DOANE BAPTIST SCHOOL A Project Presented to the COLLEGE OF INFORMATION AND COMMUNICATIONS TECHNOLOGY Western Leyte College of Ormoc City‚ Inc. A. BonifacioStreet‚ Ormoc City In Partial Fulfillment Of the Requirements for the Degree of BACHELOR OF SCIENCE IN COMPUTER SCIENCE By: CADUNGOG‚ REINA ROWENA S. TAGALOG‚ AILEEN MAE V. FEBRUARY 2013 ACKNOWLEDGMENT The researchers always expect challenges
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as high as we wanted it to be‚" says one source. After an unsuccessful search for departed CTO Adam D’Angelo’s replacement‚ Facebook finally elevated VP Mike Schroepfer to engineering lead. Facebook lost out to Twitter recuiting highly-respected algorithms engineer Pankaj Gupta. Facebook has had trouble finding a director of monetization. After speaking with a range of industry sources -- including former Facebook executives -- there appear to be three main factors contributing to Facebook’s challenges
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Since we get only a single route on solving the TSP‚ it can provide only single day itineraries. TSP-TW is a variation of TSP which addresses timing window constraints. The general class of local-search algorithms
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------------------------------------------------- Travelling salesman problem The travelling salesman problem (TSP) or travelling salesperson problem asks the following question: Given a list of cities and the distances between each pair of cities‚ what is the shortest possible route that visits each city exactly once and returns to the origin city? It is an NP-hard problem in combinatorial optimization‚ important in operations research and theoretical computer science. The problem was first formulated
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the ACO meta-heuristic called P-Evolution. P-Evolution is a modification to the ACO meta-heuristic which can be applied to a range of problem models‚ and problem instances. The working of PEvolution is demonstrated on the Travelling Salesman Problem (TSP) problem. This approach could also be used to solve dynamically changing models‚ which are representative of a number of real life systems. from the foraging behavior of biological ants. In the real world‚ ants when searching for food initially starting
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Artificial Ants 1.3 ACO Metahueristic 1.4 Applying ACO to TSP 1.4.1 Detailed implementation of TSP with ACO 1.5 Ant System and Successors 1.5.1 Elitist Ant System 1.5.2 Rank Based Ant System 1.5.3 Max-Min Ant System 1.5.3 Ant Colony System Literature survey Further scope References 1 Page No. 4 5-7 7-9 9-10 10-11 11-14 14 14-15 15-16 16-18 18-19 20-22 23 24 List of Abbreviations Abbr. Details AS ACO TSP ASrank MMAS ACS Ant System Ant Colony Optimization
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pheromone updating rule (local updating rule‚ for short) is applied.Performance:Once all the ants have generated a tour‚ the best ant deposits (at the end of iteration ) its pheromone‚ defining in this way a “preferred tour” for search in the following algorithm iteration t+1. In fact‚ during iteration t+1 ants will see edges belonging to the best tour ashighly desirable and will choose them with high probability. Still‚ guided exploration together with the fact that local updating “eats” pheromone away
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TOPIC: HEURISTIC SEARCH OVERVIEW OF HEURISTIC SEARCH Search has been vital to artificial intelligence from the very beginning as a core technique in problem solving. Current developments in search such as pattern databases and search with efficient use of external memory and parallel processing units on main boards and graphics cards are still open problems. Heuristic search as a problem solving tool is demonstrated in applications for puzzle solving‚ game playing‚ constraint satisfaction and machine
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are considered: First the fast approximate fractional Fourier transform algorithm for which two algorithms are available. The method is described in H.M. Ozaktas‚ M.A. Kutay‚ and G. Bozda˘i. Digital computation of the fractional Fourier transform. g IEEE Trans. Signal Process.‚ 44:2141–2150‚ 1996. There are two implementations: one is written by A.M. Kutay the other is part of package written by J. O’Neill. Secondly the discrete fractional Fourier transform algorithm described in the master thesis
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Operations research An introduction to solution methods Ecole des Mines de Nantes Master MOST 2012-2013 Olivier Péton - 1- Problem Min f ( x ) xS An optimization problem S is the solution set that represents all feasible solutions of a problem. f is the objective function that maps S to R. It evaluates each feasible solution. Also called evaluation function or cost function Minimization = maximization ! max f ( x) min ( f ( x)) xS xS - 2- Mathematical
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