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Artificial Intelligence

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Uptu Risers
Uptu Risers
UNIT-I Content Introduction: Introduction to Artificial Intelligence, Foundations and History of Artificial Intelligence, Applications of Artificial Intelligence, Intelligent Agents, Structure of Intelligent Agents. Computer vision, Natural Language Possessing. Introduction to Artificial Intelligence: What is Artificial Intelligence? There are many definitions; Systems that think like humans 1. Machines with minds, in the full and literal sense Systems that think rationally 1. The study of mental faculties through the use of computational models. 2. The study of the computations that make it possible to perceive, reason, and act. Systems that act like humans 1. The study of how to make computers do things that, at the moment, people are better. 2. The art of creating machines that performs functions that require intelligence when performed by people. Systems that act rationally 1. Computational intelligence is the study and design of intelligent agents. 2. Intelligent behavior in artifacts AI has many intersections with other disciplines, and many approaches to the AI problem 1. Systems that think like humans Most closely related to the field of cognitive science. We need to get inside the actual workings of the human mind and implement this in the computer. One approach is by psychological experiment, the other by introspection. Still another is biologically to reconstruct a computer brain in the same manner as human brains. 2. Systems that act humanely Under this approach the goal is to create a system that acts the same way that humans do, butmay be implemented in a totally different way. We’ll see the Turing Test shortly which is a way to determine if a system achieves the goal of acting humanely without regard to internal representations. For example, a system might appear to act like a human by inserting random typing errors, but doesn’t actually make errors the same way that a human would. 3. Systems that think rationally There is a tradition of using the ―laws of thought‖ that dates back to Socrates and Aristotle. Their study initiated the field of logic. The logic is the tradition within AI hopes to build on this approach to create intelligent systems; the main problem has been scaling this approach up beyond toy systems. By: DEVESH PANDEY email:devsiddh@gmail.com Page 1
4. Systems that act rationally An agent is something that acts. To distinguish an agent from any other program it is intended to perceive its environment, adapt to change, and operate autonomously. A rational agent is one that acts to achieve the best outcome, or best expected outcome when there is uncertainty. Unlike the ―laws of thought‖ approach, these agents might act on incomplete knowledge or to still act when it is not possible to prove what the correct thing to do is. This approach makes it more general than the ―laws of thought‖ approach and more amenable to scientific development than the pure ―human-based‖ approach. “A rational agents is one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outecome” Agent-based activity has focused on the issues of: 1) Autonomy. Agents should be independent and communicate with others as necessary. 2) Situated. Agents should be sensitive to their own surroundings and context. 3) Interactional. Often an interface with not only humans, but also with other agents. 4) Structured. Agents cooperate in a structured society. 5) Emergent. Collection of agents more powerful than an individual agent. Another way to think about the field of AI is in term of task domains: 1. Mundane: Vision, Speech Natural Language Processing, Generation, Understanding Reasoning Motion 2. Formal: Board Game-Playing, chess, checkers, gobblet Logic Calculus Algebra Verification, Theorem Proving 3. Expert: Design, engineering, graphics Art, creativity Music Financial Analysis Consulting Classification of AI is Weak vs. Strong AI: This is essentially the human vs. non-human approach. 1) Weak AI. The study and design of machines that perform intelligent tasks. Not concerned with how tasks are performed, mostly concerned with performance and efficiency, such as solutions that are reasonable for NP-Complete problems. E.g., to make a flying machine, use logic and physics, don’t mimic a bird. 2) Strong AI. The study and design of machines that simulate the human mind to perform intelligent tasks. Borrow many ideas from psychology, neuroscience. Goal is to perform tasks the way a human might do them – which makes sense, since we do have models of human thought and problem solving. Includes psychological ideas in STM, LTM, forgetting, language, genetics, etc. Assumes that the physical symbol hypothesis holds. 3) Evolutionary AI. The study and design of machines that simulate simple creatures, and attempt to evolve and have higher level emergent behavior. For example, ants, bees, etc. By: DEVESH PANDEY email:devsiddh@gmail.com Page 2
Theoretical Foundations of AI Philosophy - Logic methods of reasoning, mind as physical system foundations of learning, language, rationality Mathematics - Formal representation and proof algorithms, computation, (un)decidability, (in)tractability, probability Economics - Utility, decision theory, game theory. Neuroscience - Physical substrate for mental activity Psychology - Phenomena of perception and motor control, experimental techniques Computer Engineering - Building fast computers Control theory- Design systems that maximize an objective function over time Linguistics - Knowledge representation, grammar Abridged History of AI McCulloch & Pitts: Boolean circuit model of brain Turing's "Computing Machinery and Intelligence" Dartmouth meeting: "Artificial Intelligence" adopted Look, Ma, no hands! Early AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine Robinson's complete algorithm for logical reasoning 1965 1966—73 AI discovers computational complexity Neural network research almost disappears 1969—79 Early development of knowledge-based systems 1980-AI becomes an industry 1986-Neural networks return to popularity 1987-AI becomes a science 1995-The emergence of intelligent agents, genetic algorithms 1943 1950 1956 1952—69 1950s AI Applications Few applications of artificial intelligence. 1. Game-playing. IBM’s deep-blue has beaten Kasparov, and we have a world-champion caliber Backgammon program. 2. Automated reasoning and theorem-proving. 3. Expert Systems. An expert system is a computer program with deep knowledge in a specific niche area that provides assistance to a user. Famous examples include DENDREAL, an expert system that inferred the structure of organic molecules from their spectrographic information, and MYCIN. 4. Machine Learning. Systems that can automatically classify data and learn from new examples has become more popular, especially as the Internet has grown and spawned applications that require personalized agents to learn a user’s interests. 5. Natural Language Understanding, Semantic Modeling. 6. Modeling Human Performance. As described earlier, machine intelligence need not pattern itself after human intelligence. 7. Planning and Robotics. Planning research began as an effort to design robots that could perform their task. For example, the Sojourner robot on Mars was able to perform some of its own navigation tasks since the time delay to earth makes real-time control impossible. 8. Languages and Environments. LISP and PROLOG were designed to help support AI, along with constructs such as object-oriented design and knowledge bases. By: DEVESH PANDEY email:devsiddh@gmail.com Page 3
Intelligent agent In artificial intelligence, an intelligent agent (IA) is an autonomous entity which observes through sensors and acts upon an environment using actuators (i.e. it is an agent) and directs its activity towards achieving goals (i.e. it is rational). Intelligent agents may also learn or use knowledge to achieve their goals. They may be very simple or very complex: a reflex machine such as a thermostat is an intelligent agent, as is a human being, as is a community of human beings working together towards a goal. Intelligent agents are often described schematically as an abstract functional system similar to a computer program. For this reason, intelligent agents are sometimes called abstract intelligent agents (AIA) to distinguish them from their real world implementations as computer systems, biological systems, or organizations. Some definitions of intelligent agents emphasize their autonomy, and so prefer the term autonomous intelligent agents. Still others (notably Russell & Norvig (2003)) considered goal-directed behavior as the essence of intelligence and so prefer a term borrowed from economics, "rational agent". Intelligent agents in artificial intelligence are closely related to agents in economics, and versions of the intelligent agent paradigm are studied in cognitive science, ethics, the philosophy of practical reason, as well as in many interdisciplinary sociocognitive modeling and computer social simulations. Simple Reflex Agent Intelligent agents are also closely related to software agents (an autonomous computer program that carries out tasks on behalf of users). The Structure of Agents A simple agent program can be defined mathematically as an agent function which maps every possible percepts sequence to a possible action the agent can perform or to a coefficient, feedback element, function or constant that affects eventual actions: Agent function is an abstract concept as it could incorporate various principles of decision making like calculation of utility of individual options, deduction over logic rules, fuzzy logic, etc. The program agent, instead, maps every possible percept to an action. The term percept to refer to the agent's perceptional inputs at any given instant. In the following figures an agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators. Classes of intelligent agents Russell & Norvig group agents into five classes based on their degree of perceived intelligence and capability 1. simple reflex agents 2. model-based reflex agents 3. goal-based agents By: DEVESH PANDEY email:devsiddh@gmail.com Page 4

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