Tetris AI 팀원 김유섭 (20111974) 류동균 (20131681) 임성훈 (20131712)

Slides:

Advertisements

Similar presentations

Negative Selection Algorithms at GECCO /22/2005.

Advertisements

Exact and heuristics algorithms

Tetris – Genetic Algorithm Presented by, Jeethan & Jun.

Tetris and Genetic Algorithms Math Club 5/30/2011.

Institute of Intelligent Power Electronics – IPE Page1 Introduction to Basics of Genetic Algorithms Docent Xiao-Zhi Gao Department of Electrical Engineering.

Non-Linear Problems General approach. Non-linear Optimization Many objective functions, tend to be non-linear. Design problems for which the objective.

Stat methodes for Susy search Daniel August Stricker-Shaver Institut für Experimentelle Kernphysik, Uni Karlsruhe 10/05/2007.

1 Lecture 8: Genetic Algorithms Contents : Miming nature The steps of the algorithm –Coosing parents –Reproduction –Mutation Deeper in GA –Stochastic Universal.

Genetic algorithms for neural networks An introduction.

Evolutionary Algorithms Simon M. Lucas. The basic idea Initialise a random population of individuals repeat { evaluate select vary (e.g. mutate or crossover)

Research Trends in Artificial Intelligence Muhammad Younas Hassan Javid Danish Shah

Genetic Algorithm for Variable Selection

Tutorial 1 Temi avanzati di Intelligenza Artificiale - Lecture 3 Prof. Vincenzo Cutello Department of Mathematics and Computer Science University of Catania.

Research Trends in AI Maze Solving using GA Muhammad Younas Hassan Javaid Danish Hussain

國立陽明大學生資學程陳虹瑋. Genetic Algorithm Background Fitness function ……. population selection Cross over mutation Fitness values Random cross over.

The Performance of Evolutionary Artificial Neural Networks in Ambiguous and Unambiguous Learning Situations Melissa K. Carroll October, 2004.

Khaled Rasheed Computer Science Dept. University of Georgia

CHAPTER 12 ADVANCED INTELLIGENT SYSTEMS © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang.

Evolutionary Robotics NEAT / HyperNEAT Stanley, K.O., Miikkulainen (2001) Evolving Neural Networks through Augmenting Topologies. Competing Conventions:

Review Topics Test 1. Background Topics Definitions of Artificial Intelligence & Turing Test Physical symbol system hypothesis vs connectionist approaches.

Evolving a Sigma-Pi Network as a Network Simulator by Justin Basilico.

C. Benatti, 3/15/2012, Slide 1 GA/ICA Workshop Carla Benatti 3/15/2012.

Cristian Urs and Ben Riveira. Introduction The article we chose focuses on improving the performance of Genetic Algorithms by: Use of predictive models.

A Comparison of Nature Inspired Intelligent Optimization Methods in Aerial Spray Deposition Management Lei Wu Master’s Thesis Artificial Intelligence Center.

Soft Computing Lecture 18 Foundations of genetic algorithms (GA). Using of GA.

TECHNOLOGY GUIDE FOUR Intelligent Systems.

Literature Review: A New Decomposition Algorithm for Threshold Synthesis and Generalization of Boolean Functions Paper by José L. Subirats, José M. Jerez,

An Introduction to Artificial Intelligence and Knowledge Engineering N. Kasabov, Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering,

Appendix B: An Example of Back-propagation algorithm

Intro. ANN & Fuzzy Systems Lecture 36 GENETIC ALGORITHM (1)

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 12-1 Chapter 12 Advanced Intelligent Systems.

PART 9 Fuzzy Systems 1. Fuzzy controllers 2. Fuzzy systems and NNs 3. Fuzzy neural networks 4. Fuzzy Automata 5. Fuzzy dynamic systems FUZZY SETS AND FUZZY.

A Genetic Differential Amplifier: Design, Simulation, Construction and Testing Seema Nagaraj and Stephen Davies University of Toronto Edward S. Rogers.

 Based on observed functioning of human brain.  (Artificial Neural Networks (ANN)  Our view of neural networks is very simplistic.  We view a neural.

Improving the Genetic Algorithm Performance in Aerial Spray Deposition Management University of Georgia L. Wu, W.D. Potter, K. Rasheed USDA Forest Service.

Introduction to Genetic Algorithm Principle: survival-of-the-fitness Characteristics of GA Robust Error-tolerant Flexible When you have no idea about solving.

Project 2: Classification Using Genetic Programming Kim, MinHyeok Biointelligence laboratory Artificial.

Logic Gates M. AL-Towaileb1. Introduction Boolean algebra is used to model the circuitry of electronic devices. Each input and each output of such a device.

D Nagesh Kumar, IIScOptimization Methods: M8L5 1 Advanced Topics in Optimization Evolutionary Algorithms for Optimization and Search.

Evolutionary Robotics The Genotype-to-Phenotype Map The genotype to phenotype map: the algorithm that transforms the genotype into the phenotype. Direct.

Neural Networks And Its Applications By Dr. Surya Chitra.

A field of study that encompasses computational techniques for performing tasks that require intelligence when performed by humans. Simulation of human.

Reinforcement Learning and Tetris Jared Christen.

Genetic Algorithms. Overview “A genetic algorithm (or GA) is a variant of stochastic beam search in which successor states are generated by combining.

Artificial Intelligence By Mr. Ejaz CIIT Sahiwal Evolutionary Computation.

Genes and Variation Genotypes and phenotypes in evolution Natural selection acts on phenotypes and does not directly on genes. Natural selection.

TECHNOLOGY GUIDE FOUR Intelligent Systems. TECHNOLOGY GUIDE OUTLINE TG4.1 Introduction to Intelligent Systems TG4.2 Expert Systems TG4.3 Neural Networks.

AN OPTIMIZATION DESIGN OF ARTIFICIAL HIP STEM BY GENETIC ALGORITHM AND PATTERN CLASSIFICATION.

Artificial Neural Networks By: Steve Kidos. Outline Artificial Neural Networks: An Introduction Frank Rosenblatt’s Perceptron Multi-layer Perceptron Dot.

George Yauneridge.  Machine learning basics  Types of learning algorithms  Genetic algorithm basics  Applications and the future of genetic algorithms.

Evolutionary Design of the Closed Loop Control on the Basis of NN-ANARX Model Using Genetic Algoritm.

Advanced AI – Session 7 Genetic Algorithm By: H.Nematzadeh.

Genetic Algorithm (Knapsack Problem)

Machine Learning & Deep Learning

HyperNetworks Engın denız usta

TECHNOLOGY GUIDE FOUR Intelligent Systems.

Forecasting The Future of Movies

Genetic Algorithm and Their Applications to Scheduling

First work in AI 1943 The name “Artificial Intelligence” coined 1956

CS621: Artificial Intelligence

شاخصهای عملکردی بیمارستان

Genetic Variation Within Populations

Chapter 12 Advanced Intelligent Systems

فرق بین خوب وعالی فقط اندکی تلاش بیشتر است

network of simple neuron-like computing elements

The Evolution of Learning Algorithms for Artificial Neural Networks

Genetic algorithms: case study

Review NNs Processing Principles in Neuron / Unit

What is Artificial Intelligence?

Presentation transcript:

Tetris AI 팀원 김유섭 ( ) 류동균 ( ) 임성훈 ( )

Index 1.What is a Tetris? 2.Implementation method 3.Choice Of Algorithm 4.Genetic Algorithm

What is a Tetris?

Implementation method Hole is a negative element.

Implementation method Height is a negative element.

Implementation method Complete Line is a positive element.

Implementation method Which is the best position?

Implementation method Variable of a hole is ‘x’ Variable of a height is ‘y’ Variable of a line is ‘z’ Weighted value

Choice of algorithm Which is the best algorithm for Tetris? Genetic AlgorithmArtificial Neural Network

Choice of algorithm Artificial Neural Network Input Output

Choice of algorithm Gene pool Select Gene Crossing Mutation

Genetic algorithm Roulette Selection Area is Fitness. If area greater than other, it is selected with a high probability.

Genetic algorithm One-Point Crossing Selected Gene Child Gene Mutation

감사합니다.