Artificial IntelligenceKelompok Keilmuan E(Game Tech dan Artificial Intelligence)

Program Studi Teknik Informatika Universitas Komputer Indonesia

2014

Kelompok Keilmuan E

• Nelly Indriani W• Irfan Maliki• Ednawati Rainarli• Hendri Karisma• Ken Kinanti Purnamasari

In fact, through our entire life we never stop learning new things. This has been

crucial for our survival, but it also stimulates our curiosity.

Apa itu tugas akhir??

Skripsi/Tugas Akhir merupakan capstone pada pendidikan tinggi (undegraduate),

untuk membangun dan menguji kemampuan dan pengetahuan selama

mengikuti pendidikan di perguruan tinggi dan latihan untuk menjadi seorang

profesional.

Kurikulum berdasarkan ACM/IEEE 2005

• Computer engineering• Computer science• Information systems • Information technology• Software engineering

Tujuan

• Educational• Research

Educational1. develop your critical thinking; 2. enhance your ability to work independently; 3. increase your understanding of how to use and

appreciate scientific methods as tools for problem solving; and

4. develop your presentation skills, oral as well as written. With “critical thinking” we mean the ability to approach something new in a systematic and logical way, and to use creative and diverse, yet systematic ways to approach and solve a problem

Research• you will deepen your understanding of the

subject area, and contribute to the common knowledge and understanding of the subject area

• your project must have aspects that are original. it is generally not enough to repeat the work of others, since it is regarded as a waste of resources (time, money etc), unless, that is, your purpose is to confirm or reject previous findings

Computer Science

“Computer science is no more about computers than astronomy is about telescopes.” , Edsger W. Dijkstra

Computer Science Characteristic• An empirical discipline, in which each new artefact, e.g. a program, can be

seen as an experiment, the structure and behaviour of which can be studied.

• Concerned with a number of different issues seen from a technological perspective, e.g

• Theoretical aspects, such as numerical analysis, data structures and algorithms;

• The relationship between different pieces of software (i.e. different types of architecture, such as client-server, two-tier, three-tier, distributed system, High Performance Computing);

• Techniques and tools for developing software (i.e. software engineering, programming languages and operating systems).

Ilmu dan Rekayasa Komputer• Combinatorial• Graph Algorithm• Network• Sorting• Searhing• Learning• Artificial Intelligence• Perceptual Computing

• Recognition System• Information Theory

and Digital signal Processing

• Physics• Bioinformatics• Big Data• Etc.

Artificial Intelligence

• Problem-solving• Knowledge and reasoning• Planning• Uncertain Knowledge (probabilistic

reasoning)• Learning

AI

• Communicating• perceiving• Acting• what is AI ?

– Acting humanly– Thinking humanly– Acting rationally– Thinking rationally

Foundation

• Philosophy• Mathematics• Economics• Neuroscience• Psychology• Computer Engineering• Control theory and Cybernetics

AI

• Intelligent Agents• Searhing strategies• Optimal Decisions/Strategies in Game• Machine Learning

– Information Theory– Probabilistic Model– Graph Model

Applications• Speech Recognition/Natural Language Processing• Digital Signal Processing• Computer Vision• Perceptual Computing• Social Analytic• Prediction• Clustering• Security• Biometrics• Knowledge Finder (Datamining)• Business Intelligence• etc...

Learning• Inductive

– proses belajar berdasarkan informasi yang spesifik guna mendapatkan pola dalam menyelesaikan masalah tersebut, dan pola yang didapatkan dari informasi-informasi spesifik tersebut digunakan untuk menyelesaikan masalah yang baru/akan datang (umum).

– Machine Learning (future)• Deductive

– proses belajar berdasarakan informasi yang general untuk menyelesaikan masalah yang spesifik.

– Sistem Pakar (dead)

Machine Learning• Supervised • Unsupervised• Reinforcement Learning

Karakteristik Supervised• Masalah yang diselesaikan biasanya berbentuk

klasifikasi, dataset yang dimiliki oleh kasus yang berbentuk klasifikasi biasanya selain memiliki atribut untuk setiap instancesnya, namun juga sudah memiliki kelas yang jelas.

• Sehingga task selanjutnya dari hipotesis atau model yang ditemukan adalah melakukan klasifikasi terhadap instance yang baru dan belum memiliki label (belum diklasifikasi).

• ID3, C4.5, Artifcial Neural Network, Support Vector Machine dan lain-lain.

Karakteristik Unsupervised• Unsupervised Learning biasanya memiliki kata

kunci clustering atau melakukan pengklusteran terhadap sekelompok data atau sekelompok instances yang tidak memiliki label.

• Sehingga memiliki informasi bahwa terdapat sekumpulan data yang membentuk cluster.

• Contohnya seperti K-Mean, Graph Algorithm, EM (Expectation Maximization) Algorithm.

Karakteristik Reinforcement Learning

Biasanya berupa permasalah yang membutuhkan aktifitas eksplorasi,

sehingga cukup sesusai jika digunakan untuk membangun suatu intelijen pada

suatau game (terutama puzzle).

Fundamentals of Algorithmic Problem Solving

• Understanding the Problem• Ascertaining the Capabilities of the Computational

Device• Choosing between Exact and Approximate Problem

Solving• Algorithm Design Techniques (strategy)• Designing an Algorithm and Data Structures• Methods of Specifying an Algorithm (exp: pseudocode )• Proving an Algorithm’s Correctness• Analyzing an Algorithm• (efficiency, effectivity, simplycity, generality)• Coding an Algorithm

GOAL Dari CS?

• PERFORMANCE....– EFEKTIVITAS...– EFISIENSI...

Thanks...Good Luck...

http://about.me/hendriKarisma