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Artificial Intellige nce. Safeen H. Rasool Colla ge of Science. Course outline. Introduction, Definition, Goals, Techniques, Branches, and Applications. Problem Solving through search Search and Control Strategies Knowledge Representation and Reasoning Planning
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Artificial Intelligence SafeenH. Rasool Collage of Science
Course outline • Introduction, Definition, Goals, Techniques, Branches, and Applications. • Problem Solving through search • Search and Control Strategies • Knowledge Representation and Reasoning • Planning • Representing and Reasoning with Uncertain Knowledge • Decision-Making • Machine Learning and Knowledge Acquisition • Expert Systems • Natural Language
Introduction to AI • One thing it could be is "Making computational models of human behavior". Since we believe that humans are intelligent. • In this way of thinking of AI, how would you proceed as an AI scientist? • One way, which would be a kind of cognitive science, is to do experiments on humans, see how they behave in certain situations and see if you could make computers behave in that same way. • The research strategy is to affiliate with someone who does experiments that reveal something about what goes on inside people's heads and then build computational models that mirror those kind of processes.
Introduction to AI • Artificial intelligence (AI) may be defined as the branch of computer science that is concerned with the automation of intelligent behavior. • as such, must be based on sound theoretical and applied principles of that field. These principles include the • data structures used in knowledge representation, • algorithms needed to apply that knowledge, • languages and programming techniques used in their implementation.
The adoption of Artificial Intelligence (AI) technologies is widely expanding in our society. • Applications of AI include: self-driving cars, personal assistants, surveillance systems, robotic manufacturing, machine translation, financial services, cyber security, web search, video games, code analysis and product recommendations. • Such applications use AI techniques to interpret information from a wide variety of sources and use it to enable intelligent, goal-directed behavior.
What is Artificial Intelligence? • According to the father of Artificial Intelligence, John McCarthy, it is • “The science and engineering of making intelligent machines, especially intelligent computer programs”. • Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think.
Goals of artificial intelligence 1. Intelligent execution of user requests in the operating system.2. To provide reliability and better support to customers.3. To Create systems that understand, think, learn, and behave like humans for the purpose of learning, behaviour etc.
Artificial Intelligence Techniques 1. Heuristics • Involve or serving as an aid to learning, discovery, or problem-solving by experimental and especially trial and error methods. • whenever problems get too complex to find the guaranteed best possible solution using exact methods, Heuristics serves to employ a practical method for finding a solution that is not guaranteed to be optimal. • Suppose we have coins with the following denominations: 5 cents, 4 cents, 3 cents, and 1 cent, and that we need to determine the minimum number of coins to create the amount of 7 cents.
Artificial Intelligence Techniques 2. Support Vector Machines • In these types of problems, the objective is to determine whether a given data point belongs to a certain class or not. • After first training a classifier model on data points for which the class is known. • you can then use the model to determine the class of new, unseen data-points. • A powerful technique for these types of problems is Support Vector Machines (SVM). • The question whether an email is spam or not is an example of a classification problem, a set of emails that are labeled as spam or not spam.
Artificial Intelligence Techniques 3. Artificial Neural Networks Artificial Neural Networks (ANN) can be described as processing devices that are loosely modeled after the neural structure of a brain. The biggest difference between the two is that the ANN might have hundreds or thousands of neurons, whereas the neural structure of an animal or human brain has billions.
Artificial Intelligence Techniques 4. Markov Decision Process A Markov Decision Process (MDP) is a framework for decision-making modeling where in some situations the outcome is partly random and partly based on the input of the decision maker. The basic goal of MDP is to find a policy for the decision maker, indicating what particular action should be taken at what state.An MDP model consists of the following parts:
Artificial Intelligence Techniques 4. Markov Decision Process • A set of possible states: for example, this can refer to a grid world of a robot or the states of a door (open or closed). • A set of possible actions: a fixed set of actions that e.g. a robot can take, such as going north, left, south or west. Or with respect to a door, closing or opening it. • Transition probabilities: this is the probability of going from one state to another. For example, what is the probability that the door is closed, after the action of closing the door has been performed? • Rewards: these are used to direct the planning. For instance, a robot may want to move north to reach its destination. Actually going north will result in a higher reward.
Artificial Intelligence Techniques 5. Natural Language Processing Natural Language Processing (NLP) is used to refer to everything from speech recognition to language generation, each requiring different techniques.
Branches of Artificial Intelligence • some branches are surely missing, because no-one has identified them yet. • Some of these may be regarded as concepts or topics rather than full branches.
Branches of Artificial Intelligence • Data mining :Mining information from data: A present-day gold rush. Data Mining is a multidisciplinary field which supports knowledge workers who try to extract information in our “data rich, information poor”. • Genetic algorithm is a heuristic search method used in artificial intelligence and computing. It is used for finding optimized solutions to search problems based on the theory of natural selection and evolutionary biology. • Fuzzy logic is an approach to computing based on "degrees of truth" rather than the usual "true or false" (1 or 0) Boolean logic on which the modern computer is based. • expert system is a computer program that uses artificial intelligence (AI) technologies to simulate the judgment and behavior of a human or an organization that has expert knowledge and experience in a particular field. • Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. in the context of cellular robotic systems.
Branches of Artificial Intelligence • Logical AI What a program knows about the world in general the facts of the specific situation in which it must act, and its goals are all represented by sentences of some mathematical logical language. • The program decides what to do by inferring that certain actions are appropriate for achieving its goals. • Search AI programs often examine large numbers of possibilities, e.g. moves in a chess game or inferences by a theorem proving program. Discoveries are continually made about how to do this more efficiently in various domains. • Genetic programming Genetic programming is a technique for getting programs to solve a task by mating random Lisp programs and selecting fittest in millions of generations.
Branches of Artificial Intelligence • Pattern recognition • When a program makes observations of some kind, it is often programmed to compare what it sees with a pattern. For example, a vision program may try to match a pattern of eyes and a nose in a scene in order to find a face. • Representation • Facts about the world have to be represented in some way. Usually languages of mathematical logic are used. • knowledge and reasoning • This is the area in which AI is farthest from human-level, in spite of the fact that it has been an active research area since the 1950s.
Branches of Artificial Intelligence • Learning from experience • The approaches to AI based on connectionism and neural netsspecialize in that. There is also learning of laws expressed in logic. • Planning • Planning programs start with general facts about the world (especially facts about the effects of actions), facts about the particular situation and a statement of a goal. • Epistemology This is a study of the kinds of knowledge that are required for solving problems in the world. • Ontology Ontology is the study of the kinds of things that exist. In AI, the programs and sentences deal with various kinds of objects, and we study what these kinds are and what their basic properties are.
Applications of Artificial Intelligence • Game Playing: Much of the early research in state space search was done using common board games such as checkers, chess, and the 15-puzzle. • Natural Language Processing − It is possible to interact with the computer that understands natural language spoken by humans. • Vision Systems − These systems understand, interpret, and comprehend visual input • Speech Recognition − Some intelligent systems are capable of hearing and comprehending the language in terms of sentences • Handwriting Recognition − The handwriting recognition software reads the text written on paper by a pen or on screen by a stylus. It can recognize the shapes of the letters and convert it into editable text. • Intelligent Robots − Robots are able to perform the tasks given by a human. They have sensors to detect physical data from the real world such as light, heat, temperature …….etc
Out of the following areas, one or multiple areas can contribute to build an intelligent system.
