Intelligent Systems - Module 1 PDF

**INTELLIGENT SYSTEMS** **MODULE 1** **CHAPTER 1 -- INTRODUCTION TO INTELLIGENT SYSTEMS** **UNIT OBJECTIVES** to be aware of the rationale of the artificial intelligence and soft computing paradigms with their advantages over traditional computing to gain an understanding of the theoretical...

**INTELLIGENT SYSTEMS** **MODULE 1** **CHAPTER 1 -- INTRODUCTION TO INTELLIGENT SYSTEMS** **UNIT OBJECTIVES** to be aware of the rationale of the artificial intelligence and soft computing paradigms with their advantages over traditional computing to gain an understanding of the theoretical foundations of various types of intelligent systems technologies to a level adequate for achieving objectives as stated below **CONTENT** 1\. Introduction to Intelligent Systems 1.1. What is intelligence? 1.2. What is Intelligence Composed of? 1.3. Difference between Human and Machine Intelligence 1.4. What is an intelligent system? 1.5. Significance of intelligent systems in business 1.6. Characteristics of intelligent systems 1.7. The field of Artificial Intelligence (AI) 1.8. The Soft Computing paradigm **1.1 What is intelligence?** Intelligence is the ability of a system to calculate, reason, perceive relationships and analogies, learn from experience, store and retrieve information from memory, solve problems, comprehend complex ideas, use natural language fluently, classify, generalize, and adapt new situations.  **1.2 What is Intelligence composed of?** **The intelligence is intangible. It is composed of:** a. a) Reasoning b\) Learning c\) Problem Solving d\) Perception e\) Linguistic Intelligence a. **a) Reasoning** − It is the set of processes that enables us to provide basis for judgement, making decisions, and prediction. There are broadly two types: a. **b) Learning** − It is the activity of gaining knowledge or skill by studying, practising, being taught, or experiencing something. Learning enhances the awareness of the subjects of the study. The ability of learning is possessed by humans, some animals, and AI-enabled systems. a. **c) Problem Solving** − It is the process in which one perceives and tries to arrive at a desired solution from a present situation by taking some path, which is blocked by known or unknown hurdles. Problem solving also includes decision making, which is the process of selecting the best suitable alternative out of multiple alternatives to reach the desired goal are available. a. **d) Perception** − It is the process of acquiring, interpreting, selecting, and organizing sensory information. Perception presumes sensing. In humans, perception is aided by sensory organs. In the domain of AI, perception mechanism puts the data acquired by the sensors together in a meaningful manner. a. **e) Linguistic Intelligence** − It is one's ability to use, comprehend, speak, and write the verbal and written language. It is important in interpersonal communication. **1.3 Difference between Human and Machine Intelligence** 1\. Humans perceive by patterns whereas the machines perceive by set of rules and data. 2\. Humans store and recall information by patterns, machines do it by searching algorithms. For example, the number 40404040 is easy to remember, store, and recall as its pattern is simple. 3\. Humans can figure out the complete object even if some part of it is missing or distorted; whereas the machines cannot do it correctly. **1.4 What is an intelligent system?** **What is an intelligent system?** A truly intelligent system adapts itself to deal with changes in problems (automatic learning). They are technologically advanced machines that perceive and respond to the world around them. Intelligent systems display machine-level intelligence, reasoning, often learning, not necessarily self-adapting. A machine intelligence has a computer follow problem solving processes something like that in humans. Intelligent systems are revolutionizing a variety of industries, including transportation and logistics, security, and manufacturing. They help improve energy efficiency, quality, and flexibility of these systems. Intelligent systems are complex and use a wide range of technologies -- artificial intelligence, cybersecurity, natural language processing, deep learning, embedded CPUs, distributed storage, wireless networking and graphical signalling. 1.5 Intelligent systems in business Intelligent systems in business utilise one or more intelligence tools, usually to aid decision making. It provides business intelligence to increase productivity and gain competitive advantage. Examples of business intelligence -- information on ▪ Customer behaviour patterns ▪ Market trend ▪ Efficiency bottlenecks Examples of successful intelligent systems applications in business: ▪ Customer service (Customer Relations Modelling) ▪ Scheduling (Ex. Mine Operations) ▪ Data mining ▪ Financial market prediction ▪ Quality control **Examples of Intelligent systems in business** - a\) HNC (now Fair Isaac) software's credit card fraud detector Falcon offers 30-70% improvement over existing methods (an example of a neural network). - b\) MetLife insurance uses automated extraction of information from applications in MITA (an example of language technology use) - c\) Personalized, Internet-based TV listings (an intelligent agent) - d\) Hyundai's development apartment construction plans FASTrak-Apt (a Case Based Reasoning project) - e\) US Occupational Safety and Health Administration (OSHA uses \"expert advisors\" to help identify fire and other safety hazards at work sites (an expert system). **1.6 Characteristics of Intelligent Systems** Intelligent systems can take many forms, from automated vacuums such as the Roomba to facial recognition programs to Lazada's personalized shopping suggestions. - 1\. Intelligent Systems possess one or more of these: ▪ Capability to extract and store knowledge ▪ Human like reasoning process ▪ Learning from experience (or training) ▪ Dealing with imprecise expressions of facts ▪ Finding solutions through processes similar to natural evolution - - natural language understanding - speech recognition and synthesis - image analysis - Most current intelligent systems are based on - rule based expert systems - one or more of the methodologies belonging to soft computing Most current intelligent systems are based on **1.7 The field of Artificial Intelligence (AI)** Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving. ▪ Primary goal: ▪ Development of software aimed at enabling machines to solve problems through human-like reasoning ▪ Attempts to build systems based on a model of knowledge representation and processing in the human mind ▪ Encompasses study of the brain to understand its structure and functions ▪ In existence as a discipline since 1956 ▪ Failed to live up to initial expectations due to ▪ inadequate understanding of intelligence, brain function ▪ complexity of problems to be solved ▪ Expert systems -- an AI success story of the 80s ▪ Case Based Reasoning systems - partial success **1.8 The Soft Computing (SC) paradigm** Soft computing is the use of approximate calculations to provide imprecise but usable solutions to complex computational problems. The approach enables solutions for problems that may be either unsolvable or just too time-consuming to solve with current hardware. Also known as Computational Intelligence. Unlike conventional computing, SC techniques 1\. can be tolerant of imprecise, incomplete or corrupt input data 2\. solve problems without explicit solution steps 3\. learn the solution through repeated observation and adaptation 4\. can handle information expressed in vague linguistic terms 5\. arrive at an acceptable solution through evolution The first four characteristics are common in problem solving by individual humans. The fifth characteristic (evolution) is common in nature The core SC methodologies found in current intelligent systems are: a\) Artificial Neural Networks (ANN) b\) Fuzzy Systems c\) Genetic Algorithms (GA) **Artificial Neural Networks** Artificial Neural Networks contain artificial neurons which are called **units **. These units are arranged in a series of layers that together constitute the whole Artificial Neural Network in a system. A layer can have only a dozen units or millions of units as this depends on how the complex neural networks will be required to learn the hidden patterns in the dataset. Commonly, Artificial Neural Network has an input layer, an output layer as well as hidden layers. The input layer receives data from the outside world which the neural network needs to analyze or learn about. Then this data passes through one or multiple hidden layers that transform the input into data that is valuable for the output layer. Finally, the output layer provides an output in the form of a response of the Artificial Neural Networks to input data provided. In the majority of neural networks, units are interconnected from one layer to another. Each of these connections has weights that determine the influence of one unit on another unit. As the data transfers from one unit to another, the neural network learns more and more about the data which eventually results in an output from the output layer.  The structures and operations of human neurons serve as the basis for artificial neural networks. It is also known as neural networks or neural nets. The input layer of an artificial neural network is the first layer, and it receives input from external sources and releases it to the hidden layer, which is the second layer. In the hidden layer, each neuron receives input from the previous layer neurons, computes the weighted sum, and sends it to the neurons in the next layer. These connections are weighted means effects of the inputs from the previous layer are optimized more or less by assigning different-different weights to each input and it is adjusted during the training process by optimizing these weights for improved model performance. **Fuzzy Systems** The term "system" is usually understood as a set of interacting components with well-defined structure and organized as an intricate whole that can be distinguished from the "external" environment. A system communicates with the environment through so-called inputs and outputs. Fuzzy systems are structures based on fuzzy techniques oriented towards information processing, where the usage of classical sets theory and binary logic is impossible or difficult. In the literature, terms such as fuzzy system, fuzzy model, system based on fuzzy rules, fuzzy controller, or fuzzy associative memory are used interchangeably depending on the application type \[(https://link.springer.com/chapter/10.1007/978-3-319-59614-3_2#ref-CR16)\]. Their main characteristic involves symbolic knowledge representation in a form of fuzzy conditional (if-then) rules. The typical structure of a fuzzy system (Fig. [2.1](https://link.springer.com/chapter/10.1007/978-3-319-59614-3_2#Fig1)) consists of four functional blocks: the fuzzifier, the fuzzy inference engine, the knowledge base, and the defuzzifier. Both linguistic values (defined by fuzzy sets) and crisp (numerical) data can be used as inputs for a fuzzy system. If crisp data are applied, then the inference process is preceded by fuzzification, which assigns the appropriate fuzzy set to the nonfuzzy input. The values of input variables are mapped into linguistic values of the output variable by means of the appropriate method of approximate reasoning (inference engine) using expert knowledge, which is represented as a collection of fuzzy conditional rules (knowledge base). In addition to the linguistic values, the numerical data may be required as the fuzzy system output. In such cases defuzzification methods are used, which assign the representative crisp data to the resultant output fuzzy set. Practical applications of fuzzy systems include problems for which the complete mathematical description is unavailable, or where the usage of the precise (nonfuzzy) model is uneconomical or highly inconvenient. The ability to process inaccurate information makes a fuzzy system an excellent tool, for example, for control processes \[(https://link.springer.com/chapter/10.1007/978-3-319-59614-3_2#ref-CR12), (https://link.springer.com/chapter/10.1007/978-3-319-59614-3_2#ref-CR19)\], system identification \[(https://link.springer.com/chapter/10.1007/978-3-319-59614-3_2#ref-CR11), (https://link.springer.com/chapter/10.1007/978-3-319-59614-3_2#ref-CR20)\], decision support \[(https://link.springer.com/chapter/10.1007/978-3-319-59614-3_2#ref-CR24), (https://link.springer.com/chapter/10.1007/978-3-319-59614-3_2#ref-CR33)\], and signal and image processing \[(https://link.springer.com/chapter/10.1007/978-3-319-59614-3_2#ref-CR4), (https://link.springer.com/chapter/10.1007/978-3-319-59614-3_2#ref-CR23)\]. **Genetic Algorithms** Genetic Algorithms(GAs) are adaptive heuristic search algorithms that belong to the larger part of evolutionary algorithms. Genetic algorithms are based on the ideas of natural selection and genetics. These are intelligent exploitation of random searches provided with historical data to direct the search into the region of better performance in solution space. **They are commonly used to generate high-quality solutions for optimization problems and search problems.** Genetic Algorithms(GAs) are adaptive heuristic search algorithms that belong to the larger part of evolutionary algorithms. Genetic algorithms are based on the ideas of natural selection and genetics. These are intelligent exploitation of random searches provided with historical data to direct the search into the region of better performance in solution space. **They are commonly used to generate high-quality solutions for optimization problems and search problems.**