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Course Year : T0423-Current Popular IT III : 2013 Introduction to Artificial Intelligence and Soft Computing Session 1 Lecture Outline • • • • Introduction to Artificial Intelligence Introduction to Hard and Soft Computing The utility of Fuzzy systems Introduction to Fuzzy Logic Bina Nusantara University 3 Lecturer • Dr. Widodo Budiharto D2637 Email : [email protected] HP :08569887384 • Quiz 3x • TM 3x • Final Project presentation(Group) at session 13 • Reference book: http://ruangbacafmipa.staff.ub.ac.id/files/2012/02/ebooksclub.org__Fuzzy_Logi c_with_Engineering_Applications__Third_Edition.pdf Bina Nusantara University 4 Intro What is Fuzzy Logic • Demo Video Bina Nusantara University 5 Artificial Intelligence • Definition : (1) a branch of computer science dealing with the simulation of intelligent behavior in computers (2) the capability of a machine to imitate intelligent human behavior (merriam-webster.com) • The branch of computer science concerned with making computers behave like humans. (John McCarty) Bina Nusantara University 6 Artificial Intelligence Domain Area Bina Nusantara University 7 Soft Computing • Soft computing is an association of computing methodologies that includes fuzzy logic, neurocomputing, evolutionary computing, and probabilistic computing. (P. Bonissone, 2000). Bina Nusantara University 8 Soft Computing • Hard Computing (conventional) requires a precisely stated analytical model and often a lot of computation time. Many analytical models are valid for ideal cases. Real world problems exist in a non-ideal environment. • Soft computing was introduced by Prof. Lotfi Zadeh in 1992. It is a collection of some biologically-inspired methodologies such as Fuzzy Logic, Neural Network, Genetic Algorithm, and combined forms such as GA-FL, GA-NN, NN-FL. Bina Nusantara University 9 Hard Vs Soft Computing • Hard computing based on binary logic, crisp systems, numerical analysis and crisp software (exp. PID Controller) but soft computing based on fuzzy logic, neural nets and probabilistic reasoning. • Soft computing is tolerant of imprecision, uncertainty, partial truth, and approximation. • Hard computing requires exact input data; soft computing can deal with ambiguous and noisy dat Bina Nusantara University 10 Demo Hard computing • Demo Video Bina Nusantara University 11 A review of Soft Computing components Bina Nusantara University 12 Methods of Soft Computing • • • • • Fuzzy Logic Neural Network Theory Probabilistic Reasoning Evolutionary Computing Genetic Algorithm Bina Nusantara University 13 Evolutionary computing • Is a subfield of artificial intelligence (computational intelligence) that involves continuous optimization and combinatorial optimization problems. • Evolutionary computing uses iterative progress, such as growth or development in a population. This population is then selected in a guided random search using parallel processing to achieve the desired end. • Such processes are often inspired by biological mechanisms of evolution. • Evolutionary programming was introduced by Lawrence J. Fogel in the US, while John Henry Holland called his method a genetic algorithm Bina Nusantara University 14 The Utility of Fuzzy Systems • Several sources have shown and proven that fuzzy systems are universal approximators. Hence, fuzzy systems are very useful in two general contexts: (1) in situations involving highly complex systems whose behaviors are not well understood, and (2) In situations where an approximate, but fast, solution is warranted. Bina Nusantara University 15 Fuzzy Logic • Fuzzy logic is a form of many-valued logic or probabilistic logic; it deals with reasoning that is approximate rather than fixed and exact. In contrast with traditional logic they can have varying values, where binary sets have two-valued logic, true or false, fuzzy logic variables may have a truth value that ranges in degree between 0 and 1. • Fuzzy logic has been extended to handle the concept of partial truth, where the truth value may range between completely true and completely false Bina Nusantara University 16 Traditional logic Slow Speed = 0 Fast Speed = 1 bool speed; get the speed if ( speed == 0) { // speed is slow } else { // speed is fast } Bina Nusantara University 17 Fuzzy Logic representation Slowest For every problem must represent in terms of fuzzy sets. What are fuzzy sets? [ 0.0 – 0.25 ] Slow [ 0.25 – 0.50 ] Fast [ 0.50 – 0.75 ] Fastest [ 0.75 – 1.00 ] Bina Nusantara University 18 Why using Fuzzy Logic • Fuzzy logic allows for approximate values and inferences as well as incomplete or ambiguous data (fuzzy data) as opposed to only relying on crisp data (binary yes/no choices). • Fuzzy Logic provides a more efficient and resourceful way to solve Control Systems. • On 2000, Fuzzy Logic Becomes a Standard Technology and Is Also Applied in Data and Sensor Signal Analysis. Bina Nusantara University 19 Application of Fuzzy Logic • Sendai Subway system in Sendai, Japan. This control of the Nanboku line, developed by Hitachi, used a fuzzy controller to run the train all day long. • The most tangible applications of fuzzy logic control have appeared commercial appliances. Specifically, but not limited to heating ventillation and air conditioning (HVAC) systems. • Fuzzy logic also finds applications in many other systems. For example, the MASSIVE 3D animation system for generating crowds uses fuzzy logic for artificial intelligence. Bina Nusantara University 20 History (1965 by Zadeh) Bina Nusantara University 21 Fuzzy Sets and Membership • Classical sets contain objects that satisfy precise properties of membership; fuzzy sets contain objects that satisfy imprecise properties of membership, i.e., membership of an object in a fuzzy set can be approximate. • For example, the set of heights from 5 to 7 feet is precise (crisp); the set of heights in the region around 6 feet is imprecise, or fuzzy. Bina Nusantara University 22 Fuzzy Sets and Membership (2) • For crisp sets (Himpunan klasik) an element x in the universe X is either a member of some crisp set A or not. This binary issue of membership can be represented mathematically with the indicator function, Nilai keanggotaan Fungsi keanggotaan Bina Nusantara University 23 Fuzzy Sets Theory • Classical Set vs Fuzzy set Membership value Membership value 1 1 0 0 175 Height(cm) 175 Height(cm) Universe of discourse 24 Fuzzy Sets and Membership (4) • The membership function embodies the mathematical representation of membership in a set, and the notation used throughout this text for a fuzzy set is a set symbol with a tilde underscore, where the functional mapping is given by : • And the symbol is the degree of membership of element x in fuzzy set A. Bina Nusantara University 25 example • the meanings of the expressions cold, warm, and hot are represented by functions mapping a temperature scale. Bina Nusantara University 26 Example http://petro.tanrei.ca/fuzzylogic/fuzzy_negnevistky.html • The problem is to estimate the level of risk involved in a software engineering project. For the sake of simplicity we will arrive at our conclusion based on two inputs: project funding and project staffing. Bina Nusantara University 27 Project Funding • Suppose our our inputs are project_funding = 35% and project_staffing = 60%. Bina Nusantara University 28 Project Staffing Bina Nusantara University 29 The rules • If project_funding is adequate or project_staffing is small then risk is low. • If project_funding is marginal and project_staffing is large then risk is normal. • If project_funding is inadequate then risk is high. Bina Nusantara University 30 Rule Evaluation results Bina Nusantara University 31 Rule Evaluation results Bina Nusantara University 32 Rule Evaluation results Bina Nusantara University 33 calculation • We perform a union on all of the scaled functions to obtain the final result Bina Nusantara University 34 Result • The defuzzification can be performed in several different ways. The most popular method is the centroid method. Bina Nusantara University 35 Result • We chose the centroid method to find the final non-fuzzy risk value associated with our project. This is shown below. • The result is that this project has 67.4% risk associated with it given the definitions above. Bina Nusantara University 36 Fuzzy Logic Type 2 To handle uncertainty better than Fuzzy Logic 37 Homework • Develop simple Fuzzy logic program using example in C: http://www.chebucto.ns.ca/Science/AIMET/archive/ddj/fuzz y_logic_in_C/ Bina Nusantara University 38 References • Fuzzy Logic with Engineering Applications, chapter 1. • Artificial Inteligence : A Modern Approach, chapter 1. • http://www.seattlerobotics.org/encoder/mar98/fuz/flindex. html • http://www.soft-computing.de/def.html Bina Nusantara Projects paper (Group) • Create a paper and Discuss at session 10 and 13 (submission) • Example topics : • • • • Games using fuzzy logic Decision system using fuzzy Interval type 2 FLC Obstacle avoidance for mobile robot using fuzzy Example paper : Octavia George, Maria Gorethi, Syerra Riswandi and Widodo Budiharto, The Development of an Expert Mood Identifier System using Fuzzy Logic on BlackBerry Platform, Journal of Computer Science, vol 9(6), pp. 733739, USA, 2013. Indexed by SCOPUS Bina Nusantara University 40