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BEE4333 Intelligent Control Hamzah Ahmad Ext: 2055/2141 Course Outline Reference Achievements :PO1, CO1, CO5, LO1 Chapter 1 ARTIFICIAL INTELLIGENCE: WHAT IS IT ALL ABOUT? Today contents and achievements 1.1 Overview of artificial intelligence (AI) • LO1 : Able to understand intelligent system and its application According to the Oxford and Penguin English Dictionaries the word “intelligence” can be defined as follows: ability to understand reason perceive quickness in learning mental alertness ability to grasp relationships clever information news One way to understand “intelligence” is by looking at our own capabilities, which means that humans are able to: think understand recognize perceive generalize adapt learn make decisions solve daily problems Artificial Intelligence (AI) ? AI is a study about inventing machines/computers that capable of mimicking human/animal intelligent behavior. http://xmb.stuffucanuse.com/xmb/viewthread.php?tid=6825 The ultimate objective is to develop a system that can think and act rationally like humans. How do we design Intelligence? • Study from biological models (brain, genetic, DNA, life, Molecular biology, ….) neural nets, GA, Artificial Life, DNA Computing, Quantum Computing, Robotics, etc. • Study from human phenomena (common sense, reasoning, predicting, observing, inference, …) fuzzy logic, expert systems, search techniques, etc. • Need to develop mathematical/logical algorithms based on the above biological models or phenomena Achievements :PO1, CO1, CO5, LO2 Chapter 1 ARTIFICIAL INTELLIGENCE: COMPARISON BETWEEN CLASSICAL CONTROL AND INTELLIGENT CONTROL Today contents and achievements 1.2 Artificial intelligence applications 1.3 Comparison with classical controller • LO1 : Able to understand intelligent system and its application • LO2 : Able to compare classical control system and modern intelligent system Intelligent Control Classical Control Classical Control Intelligent Control Basic Concept Characteristics Examples of Methods Abstract Modeling - Designer input the Mathematical Modeling - Designer behavior to the system and then designed the system which includes system attempt to abstractly define the system dynamics system Need to know prior information about Does not need to know all about the the system dynamics system dynamics and conditions Suitable for system that can be easily model Appropriate for complex system Open loop system Fuzzy logic Closed loop system Artificial Neural Network System Modeling Genetic Algorithm Support vector machine Swarm Intelligence Particle Intelligence Intelligent Control Classical Control Software Designer Intelligent Control Designer Software INTELLIGENCE Classical Control Some Pre-requisites in understanding AI • • • • Some mathematical background HLL Programming Discrete-time systems Some aspects of biological systems as well as philosophy and psychology • ……….. Where AI can/should be applied? • • • • Data is overwhelming/abundance Too many manual operations/procedures Optimization is possible Parallel/Distributed procedures/architectures are needed • Decision making is required • When current techniques are too complicated to be used/designed Where AI can/should be applied? .. Cont’d • Mathematical models are too complex/impossible • To increase efficiency • To reduce cost • To improve performance and reliability Some Important Facts, you need to know…. • • • • AI is not the only solution AI is only one part of technology AI is just a tool for improvement You must know your domain/target application Expert systems Fuzzy logic Neural networks GA …………. Expert systems Fuzzy logic Neural networks GA …………. Intelligent ManMachine Interface Cognition Algorithms, (Algorithms computations ) Execution Intelligent machine Perception (sensors) TASKS Intelligent Systems; Conceptual Design AI : Some of the approaches • • • • • • Expert system Fuzzy Logic Genetic Algorithm Swarm Intelligence Ant Colony etc Expert System • Expert System (ES) is a branch of Artificial Intelligence that attempt to mimic human experts specifically in decision making process based on prior knowledge. Expert systems can either support decision makers or completely replace them. Expert systems are the most widely applied & commercially successful AI technology. Types of ES • Ruled Based Expert System – Represented as a series of rules • Frame-Based System – Representation of the object-oriented programming approach • Hybrid System – Include several knowledge representation approach • Model-Based System – Structured around the model that stimulates the structure and function of the system under study • Ready-Made (Off-the-shelf) System – Custom-made, similar to application package such as an accounting general ledger or project management in operation mgmt. Rules as a knowledge representation technique The term rule in AI, which is the most commonly used type of knowledge representation, can be defined as an IF-THEN structure that relates given information or facts in the IF part to some action in the THEN part. A rule provides some description of how to solve a problem. Rules are relatively easy to create and understand. Any rule consists of two parts: the IF part, called the antecedent (premise or condition) and the THEN part called the consequent (conclusion or action). Rules can represent relations, recommendations, directives, strategies and heuristics: Relation IF the ‘fuel tank’ is empty THEN the car is dead Recommendation IF the sea is very deep AND the sky is cloudy AND the forecast is danger THEN the advice is ‘do not go to the sea’ Directive IF eat too much raya cakes, rendang AND the stomach is always aching THEN the action is ‘fasting in Syawal’ Fuzzy logic : human reasoning process (approximation) • Differs from binary set theory(true or false, or 1 or 0) • Similar to probability but not same in concept. – Fuzzy (degree of truth) – Probability (likelihood) • An elements might have partial characteristics of others or a subset of something but nonfuzzy is more deterministic. Biological Network Artificial Neural Network 1 An artificial neural network is a universal function approximator. Weights Wij are “learned” to fit any function. n x1 a1 O1 x2 a2 O2 xj ak Om • • • 1 Wji • • • n Wkj ANN INPUT Weights Computation Node(s)/Neuron OUTPUT Briefing about… AI IN INDUSTRIES 3 General Types of Industries Companies engaged in manufacturing •Automotive Parts ・Industrial Equipment •High-tech Industry ・Heavy Equipment •Planes … and others Advantages of Adding Intelligence in Products/ Systems • • • • • • • • Better performance Longer Life Reliability Simpler operation Cost effective Higher efficiency Self-organizing / self-optimization Simpler design Is there really a need for AI? Manufacturers need to improve on their products Need to satisfy customers Need to improve products’ reliability Need to improve products’ performance Need to improve products’ features Need to distinguish their products away from their competitors Made in Japan (based on a video on innovations and the need for change) 1980s • Excellent Quality • Expensive • Leadership • Balance of Payments • High Technology • Innovations 1960s • Junk • Cheap • Poor Quality • Copies • Low Technology • Imitation World Industrial Leaders by Country (1975-2000)(MIS for the Info Age) In 1994 alone Japan sold US$34 billion worth of consumer products using fuzzy logic technology ASIMO Advanced Step in Innovative MObility Camera Eyes [AI] Antenna Battery (Fuel Cell) Gyro Sensor Measuring Body Angle Actuators and Other Peripheral Systems Controlling leg movements [AI] Load Sensors In Leg Intelligent Real-time Flexible Walking [AI] The New ASIMO The key features of the new ASIMO include: Advanced communication ability thanks to pattern recognition technology 1. Recognition of moving objects 4. Sound recognition 2. Posture/gesture recognition 5. Face recognition 3. Environment recognition Mitsubishi’s- Annanova Sony’s AIBO Fujitsu’s – HOAP (Miniature Human Robot) PINO ROBOTS Sony’s SDR (Sony Dream Robots) SDR-4X II Introducing 4 new technologies • Small robot actuators (ISA-4) • Real-time Integrated Adaptive Motion Control • Motion creation software • Real-time Real World Space perception • Multi-modal Human-Robot Perception SDR - specifications Intelligent Servo Actuators (ISA-4) Other Features • Multi-face Detection Emotional Expression Some Postures of SDR Future Research in Humanoids • • • • • • • ROBOT Speed (Fast) Not tired-Can do repetitive job (Fuel Cell) Not imaginative/Not creative Better speech and pattern recognition Some emotion Entertainment Personal Friend • • • • • • • HUMAN Slow Intelligent Easily tired Imaginative/Creative Emotional Desire Etc. …… Issues to be considered… – Do not apply AI when •Lack of Data •Simpler techniques are available / sufficient •Further optimization is not possible – – – – The AI Machine faulty Are Robots More Intelligent than Humans? Can Robots Replace Humans? Human vs Machine AI Applications • Group Assignments ( 1 hour ) • Freshen up your industrial attachments… – List down industries application which use the classical control in their company. – Identify any of the application in the industries that has applied AI in their system – Propose any one of the AI method for any suitable/appropriate system in industries and explain why did you choose the proposed technique. AI Applications • Select a specific machine/system and propose AI technique to make the machine/system more intelligence. – You need to draw/sketch the machine and show where to apply the AI technique – Explain why you should apply AI in the machine/system What have you learned today? • • • • • AI in three(3) techniques; ES, Fuzzy, ANN Description about each techniques Differences between AI and Classical control Applications of AI in industries Capability to analyze and proposing new technology to the industries Achieving CO1 and LO1, LO2