Unit-1 Introduction to Artificial Intelligent [22AI002] PDF

Unit- 1 Introduction to Artificial Intelligent [22AI002] Dr. Aditi Moudgil Department of Computer Science and Engineering Chitkara University, Punjab Syllabus Unit - 1 Introduction to Artificial Intelligence [4 hrs] Introduction of Artificial Intelligence: Defi...

Unit- 1 Introduction to Artificial Intelligent [22AI002] Dr. Aditi Moudgil Department of Computer Science and Engineering Chitkara University, Punjab Syllabus Unit - 1 Introduction to Artificial Intelligence [4 hrs] Introduction of Artificial Intelligence: Definition, Goals of AI, Applications areas of AI, History of AI, Types of AI, Importance of Artificial Intelligence, Intelligent agents and environment. Unit - 2 Searching [6 hrs] Searching: Search algorithm terminologies, properties for search algorithms, Search Algorithms, Uninformed Search Algorithms, Informed Search Algorithms, Hill Climbing Algorithm, Means-Ends Analysis. Unit - 3 Knowledge [9 hrs] Knowledge-Based Agent, Architecture of knowledge-based agent, Inference system, Operations performed by Knowledge-Based Agent, Knowledge Representation, Types of Knowledge, Approaches to knowledge representation, Knowledge Representation Techniques. 2 Syllabus Unit - 4 Logic [11 hrs] Propositional Logic, Rules of Inference, The Wumpus world, knowledge-base for Wumpus World, First-order logic, Knowledge Engineering in FOL, Inference in First-Order Logic, Unification in FOL, Resolution in FOL, Forward Chaining and backward chaining, Backward Chaining vs forwarding Chaining, Reasoning in AI, Inductive vs. Deductive reasoning. Textbooks 1. Introduction to Artificial Intelligence & Expert Systems' by Dan W. Patterson, Englewood Cliffs, NJ, 1990, Prentice-Hall International. Reference Books 1. 'Artificial Intelligence’ by Elaine Rich, Kevin Knight, Shivashankar B Nair, (McGraw-Hill) 2. ‘Artificial Intelligence A Modern Approach, ‘ by Stuart J. Russell and Peter Norvig, Third Edition, Prentice-Hall. 3 Contents Unit -1 Introduction to Artificial Intelligence [4 hrs] Introduction of Artificial Intelligence: Definition, Goals of AI, Applications areas of AI, Importance of Artificial Intelligence, History of Artificial Intelligence, Types of Artificial Intelligence, Intelligent agents and environment. 4 What is Artificial Intelligence? Artificial Intelligence is composed of two words Artificial and Intelligence, where Artificial defines "man-made," and intelligence defines "thinking power", hence AI means "a man- made thinking power.“ "It is a branch of computer science by which we can create intelligent machines which can behave like a human, think like humans, and be able to make decisions." Artificial Intelligence exists when a machine can have human-based skills such as learning, reasoning, and solving problems. 5 What is Artificial Intelligence? With Artificial Intelligence you do not need to preprogram a machine to do some work, despite that you can create a machine with programmed algorithms which can work with your own intelligence, and that is the awesomeness of AI. It is believed that AI is not a new technology, and some people say that as per Greek myth, there were Mechanical men in the early days who can work and behave like humans. 6 Why Artificial Intelligence? With the help of AI, you can create software or devices which can solve real-world problems very easily and with accuracy such as health issues, marketing, traffic issues, etc. With the help of AI, you can create your personal virtual assistants, such as Cortana, Google Assistant, Siri, etc. With the help of AI, you can build such Robots which can work in an environment where the survival of humans can be at risk. AI opens a path for other new technologies, new devices, and new opportunities. 7 Goals of Artificial Intelligence 1.To Create Expert Systems − The systems which exhibit intelligent behavior, learn, demonstrate, explain, and advise their users. 2.To Implement Human Intelligence in Machines − Creating systems that understand, think, learn, and behave like humans. 3.Improving Problem-solving skills - The potential of AI in solving problems will make our lives easier as complex duties can be designated to dependable AI systems which can simplify vital jobs 8 Goals of Artificial Intelligence 4. Include knowledge representation – It is concerned with the representation of 'what is known' to machines by using the existence of a set of objects, relations, and concepts. The representation displays real-world data that a computer can utilize to solve complicated real- world problems, such as detecting a medical condition or conversing with humans in natural language. 5. Facilitates Planning − Through predictive analytics, data analysis, forecasting, and optimization models, AI-driven planning creates a procedural course of action for a system to reach its goals and optimizes overall performance. One of the principal goals of 9 Goals of Artificial Intelligence 5. Allow continuous learning - Conceptually, learning implies the ability of computer algorithms to improve the knowledge of an AI program through observations and past experiences. Technically, AI programs process a collection of input-output pairs for a defined function and use the results to predict outcomes for new inputs. AI primarily uses two learning models–supervised and unsupervised–where the main distinction lies in using labeled datasets. As AI systems learn independently, they require minimal or no human intervention. For example, ML defines an automated learning process. 10 Goals of Artificial Intelligence 6. Promote creativity AI promotes creativity and artificial thinking that can help humans accomplish tasks better. It also offers a platform to augment and strengthen creativity, as AI can develop many novel ideas and concepts that can inspire and boost the overall creative process. For example, an AI system can provide multiple interior design options for a 3D-rendered apartment layout. 11 What Comprises to AI? 12 What Comprises to AI? Artificial Intelligence is not just a part of computer science even it's so vast and requires lots of other factors which can contribute to it. To create the AI, first we should know that how intelligence is composed, so the Intelligence is an intangible part of our brain which is a combination of Reasoning, learning, problem- solving perception, language understanding, etc. 13 Application areas of AI 14 Application areas of AI 1. AI in Astronomy Artificial Intelligence can be very useful to solve complex universe problems. AI technology can be helpful for understanding the universe such as how it works, its origin, etc. 2. AI in Healthcare In the last, five to ten years, AI becoming more advantageous for the healthcare industry and going to have a significant impact on this industry. Healthcare Industries are applying AI to make a better and faster diagnosis than humans. AI can help doctors with diagnoses and can inform when patients are worsening so that medical help can reach the patient before hospitalization. 15 Application areas of AI 3. AI in Gaming AI can be used for gaming purposes. The AI machines can play strategic games like chess, where the machine needs to think of a large number of possible places. 4. AI in Finance AI and finance industries are the best matches for each other. The finance industry is implementing automation, chatbot, adaptive intelligence, algorithm trading, and machine learning into financial processes. 5. AI in Data Security The security of data is crucial for every company and cyber-attacks are growing very rapidly in the digital world. AI can be used to make your data more safe and secure. Some examples such as the AEG bot, AI2 Platform, are used to determine software bugs and 16 Application areas of AI 6. AI in Social Media Social Media sites such as Facebook, Twitter, and Snapchat contain billions of user profiles, which need to be stored and managed in a very efficient way. AI can organize and manage massive amounts of data. AI can analyze lots of data to identify the latest trends, hashtags, and requirements of different users. 7. AI in Travel & Transport AI is becoming highly demanding for travel industries. AI is capable of doing various travel-related works such as making travel arrangements to suggesting the hotels, flights, and best routes to the customers. Travel industries are using AI-powered chatbots which can make human-like interaction with customers for better and fast response. 17 Application areas of AI 8. AI in Automotive Industry Some Automotive industries are using AI to provide virtual assistants to their users for better performance. Such as Tesla has introduced TeslaBot, an intelligent virtual assistant. Various Industries are currently working for developing self-driven cars which can make your journey more safe and secure. 9. AI in Robotics: Usually, general robots are programmed such that they can perform some repetitive tasks, but with the help of AI, we can create intelligent robots which can perform tasks with their own experiences without being pre-programmed. Humanoid Robots are the best examples of AI in robotics, recently the intelligent Humanoid robot 18 Application areas of AI 10. AI in Entertainment We are currently using some AI-based applications in our daily life with some entertainment services such as Netflix or Amazon. With the help of ML/AI algorithms, these services show the recommendations for programs or shows. 11. AI in Agriculture Agriculture is an area that requires various resources, labor, money, and time for the best result. Now a day's agriculture is becoming digital, and AI is emerging in this field. Agriculture is applying AI as agriculture robotics, solid and crop monitoring, predictive analysis. AI in agriculture can be very helpful for farmers. 12. AI in E-commerce AI is providing a competitive edge to the e-commerce 19 industry, and it is becoming more demanding in the e- Application areas of AI 13. AI in education: AI can automate grading so that the tutor can have more time to teach. AI chatbot can communicate with students as a teaching assistant. AI in the future can work as a personal virtual tutor for students, which will be accessible easily at any time and any place. 20 Importance of Artificial Intelligence When we look at the importance of AI from a more philosophical perspective, we can say that it has the potential to help humans live more meaningful lives that are devoid of hard labor. AI can also help manage the complex web of interconnected individuals, companies, states, and nations to function in a manner that’s beneficial to all of humanity. Currently, Artificial Intelligence is shared by all the different tools and techniques that have been invented by us over the last thousand years – to simplify human effort, and to help us make better decisions. 21 Importance of Artificial Intelligence Artificial Intelligence is one such creation that will help us in further inventing ground-breaking tools and services that would exponentially change how we lead our lives, by hopefully removing strife, inequality, and human suffering. Artificial Intelligence is currently being used mostly by companies to improve their process efficiencies, automate resource-heavy tasks, and make business predictions based on data available to us. As you see, AI is significant to us in several ways. It is creating new opportunities in the world, helping us improve our productivity, and so much more. 22 Advantages of Artificial Intelligence High Accuracy with less errors: AI machines or systems are prone to less errors and high accuracy as it takes decisions as per pre-experience or information. High-Speed: AI systems can be of very high-speed and fast-decision making, because of that AI systems can beat a chess champion in the Chess game. High reliability: AI machines are highly reliable and can perform the same action multiple times with high accuracy. Useful for risky areas: AI machines can be helpful in situations such as defusing a bomb, exploring the ocean floor, where to employ a human can be risky. Daily Applications: Daily applications such as Apple’s Siri, Window’s Cortana, Google’s OK Google are frequently used in our daily routine whether it is for searching a location, taking a selfie, making a phone call, replying to a mail and many more. 23 Advantages of Artificial Intelligence Digital Assistant: AI can be very useful to provide digital assistant to the users such as AI technology is currently used by various E-commerce websites to show the products as per customer requirement. Useful as a public utility: AI can be very useful for public utilities such as a self-driving car which can make our journey safer and hassle-free, facial recognition for security purposes, Natural language processing to communicate with the human in human language, etc. Available 24x7: An Average human will work for 4–6 hours a day excluding the breaks. Humans are built in such a way to get some time out for refreshing themselves and get ready for a new day of work and they even have weekly offed to stay intact with their work-life and personal life. But using AI we can make machines work 24x7 without any breaks and they don’t even get bored, unlike humans. New Inventions: AI is powering many inventions in almost every domain which will help humans solve the majority of complex problems. 24 Disadvantages of Artificial Intelligence High Cost: The hardware and software requirement of AI is very costly as it requires lots of maintenance to meet current world requirements. Can't think out of the box: Even we are making smarter machines with AI, but still they cannot work out of the box, as the robot will only do that work for which they are trained, or programmed. No feelings and emotions: AI machines can be an outstanding performer, but still it does not have the feeling so it cannot make any kind of emotional attachment with human, and may sometime be harmful for users if the proper care is not taken. 25 Disadvantages of Artificial Intelligence Increase dependency on machines: With the increment of technology, people are getting more dependent on devices and hence they are losing their mental capabilities. No Original Creativity: As humans are so creative and can imagine some new ideas but still AI machines cannot beat this power of human intelligence and cannot be creative and imaginative. Unemployment: As AI is replacing the majority of the repetitive tasks and other works with robots, human interference is becoming less which will cause a major problem in the employment standards. Every organization is looking to replace the minimum qualified individuals with AI robots which can do similar work with more efficiency. 26 History of Artificial Intelligence Maturation of Artificial Intelligence (1943-1952) Year 1943: The first work which is now recognized as AI was done by Warren McCulloch and Walter pits in 1943. They proposed a model of artificial neurons. Year 1949: Donald Hebb demonstrated an updating rule for modifying the connection strength between neurons. His rule is now called Hebbian learning. Year 1950: The Alan Turing who was an English mathematician and pioneered Machine learning in 1950. Alan Turing publishes "Computing Machinery and Intelligence" in which he proposed a test. The test can check the machine's ability to exhibit intelligent behavior equivalent to human intelligence, called a Turing test 27 History of Artificial Intelligence The birth of Artificial Intelligence (1952-1956) Year 1955: An Allen Newell and Herbert A. Simon created the "first artificial intelligence program "Which was named "Logic Theorist". This program had proved 38 of 52 Mathematics theorems, and found new and more elegant proofs for some theorems. Year 1956: The word “Artificial Intelligence” was first adopted by American Computer scientist John McCarthy at the Dartmouth Conference. For the first time, AI was coined as an academic field. At that time high-level computer languages such as FORTRAN, LISP, or COBOL were invented. And the enthusiasm for AI was very high at that time. 28 History of Artificial Intelligence The golden years-Early enthusiasm (1956-1974) Year 1966: The researchers emphasized developing algorithms that can solve mathematical problems. Joseph Weizenbaum created the first chatbot in 1966, which was named ELIZA. Year 1972: The first intelligent humanoid robot was built in Japan which was named WABOT-1. The first AI winter (1974-1980) The duration between the years 1974 to 1980 was the first AI winter duration. AI winter refers to the time period where computer scientists dealt with a severe shortage of funding from the government for AI researches. During AI winters, an interest in publicity on artificial intelligence was decreased. 29 History of Artificial Intelligence A boom of AI (1980-1987) Year 1980: After AI winter duration, AI came back with an "Expert System". Expert systems were programmed that emulate the decision-making ability of a human expert. In the Year 1980, the first national conference of the American Association of Artificial Intelligence was held at Stanford University. The second AI winter (1987-1993) The duration between the years 1987 to 1993 was the second AI Winter duration. Again Investors and the government stopped funding for AI research due to high costs but not efficient results. The expert system such as XCON was very cost-effective. 30 History of Artificial Intelligence The emergence of intelligent agents (1993-2011) Year 1997: In the year 1997, IBM Deep Blue beats world chess champion, Gary Kasparov, and became the first computer to beat a world chess champion. Year 2002: for the first time, AI entered the home in the form of Roomba, a vacuum cleaner. Year 2006: AI came into the Business world till the year 2006. Companies like Facebook, Twitter, and Netflix also started using AI. 31 History of Artificial Intelligence Deep Learning, Big Data, and Artificial Intelligence (2011-present) Year 2011: In the year 2011, IBM's Watson won jeopardy, a quiz show, where it had to solve complex questions as well as riddles. Watson had proved that it could understand natural language and can solve tricky questions quickly. Year 2012: Google has launched an Android app feature "Google now", which was able to provide information to the user as a prediction. Year 2014: In the year 2014, Chatbot "Eugene Goostman" won a competition in the infamous "Turing test." 32 History of Artificial Intelligence Year 2018: The "Project Debater" from IBM debated on complex topics with two master debaters and also performed extremely well. Google has demonstrated an AI program "Duplex" which was a virtual assistant and which had taken hairdresser appointments on call, and the lady on the other side didn't notice that she was talking with the machine. Now AI has developed to a remarkable level. The concept of Deep learning, big data, and data science are now trending like a boom. Nowadays companies like Google, Facebook, IBM, and Amazon are working with AI and creating amazing devices. The future of Artificial Intelligence is inspiring and will come with high 33 intelligence. Types of Artificial Intelligence 34 AI type - 1: Based on Capabilities 1. Weak AI or Narrow AI: Narrow AI is a type of AI which is able to perform a dedicated task with intelligence. The most common and currently available AI is Narrow AI in the world of Artificial Intelligence. Narrow AI cannot perform beyond its field or limitations, as it is only trained for one specific task. Hence it is also termed as weak AI. Narrow AI can fail in unpredictable ways if it goes beyond its limits. Apple Siriis a good example of Narrow AI, but it operates with a limited pre-defined range of functions. IBM's Watson supercomputer also comes under Narrow AI, as it uses an Expert system approach combined with Machine learning and natural language 35 AI type - 1: Based on Capabilities Some Examples of Narrow AI are playing chess, purchasing suggestions on e-commerce sites, self-driving cars, speech recognition, and image recognition. 2. General AI: General AI is a type of intelligence that could perform any intellectual task with efficiency like a human. The idea behind the general AI is to make such a system that could be smarter and think like a human on its own. Currently, there is no such system exist which could come under general AI and can perform any task as perfect as a human. 36 AI type - 1: Based on Capabilities The worldwide researchers are now focused on developing machines with General AI. As systems with general AI are still under research, and it will take lots of effort and time to develop such systems. 3. Super AI: Super AI is a level of Intelligence of Systems at which machines could surpass human intelligence, and can perform any task better than humans with cognitive properties. It is an outcome of general AI. Some key characteristics of strong AI include capability include the ability to think, reason, solve the puzzle, make judgments, plan, learn, and communicate on its own. 37 AI type - 1: Based on Capabilities Super AI is still a hypothetical concept of Artificial Intelligence. The development of such systems in real is still a world-changing task. 38 AI type - 2: Based on functionality 1. Reactive Machines Purely reactive machines are the most basic types of Artificial Intelligence. Such AI systems do not store memories or past experiences for future actions. These machines only focus on current scenarios and react to them as per possible best action. IBM's Deep Blue system is an example of a reactive machine. Google’s AlphaGo is also an example of a reactive machine. 39 AI type - 2: Based on functionality 2. Limited Memory Limited memory machines can store past experiences or some data for a short period of time. These machines can use stored data for a limited time period only. Self-driving cars are one of the best examples of Limited Memory systems. These cars can store the recent speed of nearby cars, the distance of other cars, speed limit, and other information to navigate the road. 40 AI type - 2: Based on functionality 3. Theory of Mind Theory of Mind AI should understand human emotions, people, beliefs, and be able to interact socially like humans. This type of AI machine is still not developed, but researchers are making lots of efforts and improvements for developing such AI machines. 41  AI type - 2: Based on functionality 4. Self-Awareness Self-awareness AI is the future of Artificial Intelligence. These machines will be super intelligent and will have their own consciousness, sentiments, and self- awareness. These machines will be smarter than the human mind. Self-Awareness AI does not exist in reality still and it is a hypothetical concept. 42  What is an Agent? An agent can be anything that perceives its environment through sensors and act upon that environment through actuators. An Agent runs in the cycle of perceiving, thinking, and acting. An agent can be: Human-Agent: A human agent has eyes, ears, and other organs which work for sensors and hand, legs, vocal tract work for actuators. Robotic Agent: A robotic agent can have cameras, infrared range finder, NLP for sensors, and various motors for actuators. Software Agent: Software agents can have keystrokes, file contents as sensory input and act on those inputs and display output on the screen. 43 Hence the world around us is full of agents such as  What is an Agent? Sensor: A sensor is a device that detects the change in the environment and sends the information to other electronic devices. An agent observes its environment through sensors. Actuators: Actuators are the component of machines that converts energy into motion. The actuators are only responsible for moving and controlling a system. An actuator can be an electric motor, gears, rails, etc. Effectors: Effectors are the devices that affect the environment. Effectors can be legs, wheels, arms, fingers, wings, fins, and display screens. 44  Intelligent Agents An intelligent agent is an autonomous entity that acts upon an environment using sensors and actuators for achieving goals. An intelligent agent may learn from the environment to achieve their goals. A thermostat is an example of an intelligent agent. Following are the main four rules for an AI agent: Rule 1: An AI agent must have the ability to perceive the environment. Rule 2: The observation must be used to make decisions. Rule 3: Decision should result in an action. Rule 4: The action taken by an AI agent must be a 45 rational action.  Rational Agent A rational agent is an agent which has clear preference, models uncertainty, and acts in a way to maximize its performance measure with all possible actions. A rational agent is said to perform the right things. AI is about creating rational agents to use for game theory and decision theory for various real-world scenarios. For an AI agent, the rational action is most important because in the AI reinforcement learning algorithm, for each best possible action, the agent gets the positive reward and for each wrong action, an agent gets a negative reward. 46  Rational Agent Rationality: The rationality of an agent is measured by its performance measure. Rationality can be judged on the basis of the following points: Performance measure which defines the success criterion. Agent prior knowledge of its environment. Best possible actions that an agent can perform. The sequence of percepts. 47  Structure of an AI Agent The task of AI is to design an agent program that implements the agent function. The structure of an intelligent agent is a combination of architecture and agent programs. It can be viewed as: Agent = Architecture + Agent program Architecture: Architecture is machinery that an AI agent executes on. Agent Function: The agent function is used to map a percept to an action. f:P→A Agent program: The agent program is an implementation of the agent function. An agent program48  PEAS Representation PEAS is a type of model on which an AI agent works upon. When we define an AI agent or rational agent, then we can group its properties under the PEAS representation model. It is made up of four words: P: Performance measure E: Environment A: Actuators S: Sensors Here performance measure is the objective for the success of an agent's behavior. 49  PEAS for self-driving cars Let's suppose a self-driving car then PEAS representation will be: Performance: Safety, time, comfort Environment: Roads, other vehicles, road signs, pedestrian Actuators: Steering, accelerator, brake, signal, horn Sensors: Camera, GPS, speedometer, odometer, accelerometer, sonar. 50 Example of Agents with their PEAS representation Agent Performan Environme Actuators Sensors ce nt measure 1. Medical Healthy Patient Tests Keyboard Diagnose patient Hospital Treatments (Entry of Minimized Staff symptoms) cost 2. Vacuum Cleanness Room Wheels Camera Cleaner Efficiency Table Brushes Dirt Battery life Wood floor Vacuum detection Security Carpet Extractor sensor Various Cliff sensor obstacles Bump Sensor Infrared Wall Sensor 3. Part - Percentage Conveyor Jointed Camera 51  Agent Environment in AI An environment is everything in the world that surrounds the agent, but it is not a part of an agent itself. An environment can be described as a situation in which an agent is present. The environment is where the agent lives, operates and provides the agent with something to sense and act upon it. 52  Types of AI Agents Agents can be grouped into five classes based on their degree of perceived intelligence and capability. All these agents can improve their performance and generate better action over time. These are given below: Simple Reflex Agent Model-based reflex agent Goal-based agents Utility-based agent Learning agent 53  1. Simple Reflex agent The Simple reflex agents are the simplest agents. These agents take decisions on the basis of the current percepts and ignore the rest of the percept history. These agents only succeed in the fully observable environment. The Simple reflex agent does not consider any part of percepts history during their decision and action process. The Simple reflex agent works on the Condition- action rule, which means it maps the current state to action. Such as a Room Cleaner agent, it works only if there is dirt in the room. Problems for the simple reflex agent design approach:54  1. Simple Reflex agent They do not have knowledge of non-perceptual parts of the current state Mostly too big to generate and to store. Not adaptive to changes in the environment. 55  2. Model-based reflex agent The Model-based agent can work in a partially observable environment, and track the situation. A model-based agent has two important factors: Model: It is knowledge about "how things happen in the world," so it is called a Model- based agent. Internal State: It is a representation of the current state based on percept history. These agents have the model, "which is knowledge of the world" and based on the model they perform actions. Updating the agent state requires information about: 56 How the world evolves 2. Model-based reflex agent 57  3. Goal-based agents The knowledge of the current state environment is not always sufficient to decide for an agent to what to do. The agent needs to know its goal which describes desirable situations. Goal-based agents expand the capabilities of the model-based agent by having the "goal" information. They choose an action, so that they can achieve the goal. These agents may have to consider a long sequence of possible actions before deciding whether the goal is achieved or not. Such considerations of different scenario are called 58 searching and planning, which makes an agent 3. Goal-based agents 59  4. Utility-based agents These agents are similar to the goal-based agent but provide an extra component of utility measurement which makes them different by providing a measure of success at a given state. Utility-based agent act based not only goals but also the best way to achieve the goal. The Utility-based agent is useful when there are multiple possible alternatives, and an agent has to choose in order to perform the best action. The utility function maps each state to a real number to check how efficiently each action achieves the goals. 60 4. Utility-based agents 61  5. Learning Agents A learning agent in AI is the type of agent which can learn from its past experiences or has learning capabilities. It starts to act with basic knowledge and then can act and adapt automatically through learning. A learning agent has mainly four conceptual components, which are: Learning element: It is responsible for making improvements by learning from the environment. Critic: The learning element takes feedback from critics which describe that how well the agent is doing with respect to a fixed performance standard. 62  5. Learning Agents Performance element: It is responsible for selecting external action Problem generator: This component is responsible for suggesting actions that will lead to new and informative experiences. Hence, learning agents are able to learn, analyze performance, and look for new ways to improve performance. 63 5. Learning Agents 64 THANK YOU 65