Introduction to Artificial Intelligence, Lecture notes of Artificial Intelligence

Download Introduction to Artificial Intelligence and more Lecture notes Artificial Intelligence in PDF only on Docsity! Fundamentals of Artificial Intelligence Introduction to Artificial Intelligence What is Artificial Intelligence? • The art of creating machines that perform functions that require intelligence when performed by people. • The branch of computer science that is concerned with the automation of intelligent behavior. • Views of AI fall into four categories: • Systems that act like humans 
 • Systems that think like humans 
 • Systems that think rationally • Systems that act rationally Acting humanly: The Turing Test • Turing Test, proposed by Alan Turing (1950), was designed to provide a satisfactory operational definition of intelligence • Can machines think?!Can machines behave intelligently? • Operational test for intelligent behavior: the Imitation Game • Suggested major components of AI system: knowledge representa t ion , automated reasoning, natural language understanding, machine learning. Thinking humanly: Cognitive Science • The field of cognitive science brings together computer models from AI and • Second, there is a big difference between solving a problem “in principle” and solving it in practice. Acting rationally: The rational agent approach • An agent is just something that acts. • A rational agent is one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome. • In the “laws of thought” approach to AI, the emphasis was on correct inferences. • Making correct inferences is sometimes part of being a rational agent, and 
 • There are also ways of acting rationally that cannot involve inference. • Rational behavior: doing the right thing 
 • The right thing: that which is expected to maximize goal achievement, given the available information • Doing the right thing doesn't necessarily always involve thinking but thinking should be in the service of rational action Acting rationally: Rational agents • An agent is an entity that perceives and acts 
 • This AI course concentrates on general principles of rational agents and on components for constructing them. 
 • Abstractly, an agent is a function from percept histories to actions: f : P* → A 
 • For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance. 
 • Computational limitations can make perfect rationality unachievable ! Design best program for given machine resources 
 Foundations of AI • Many disciplines contribute to a foundation for artificial intelligence. • Philosophy: logic, methods of reasoning, mind as physical system 
 • Mathematics: formal representation and p r o o f , a l g o r i t h m s , c o m p u t a t i o n , decidability, probability 
 • McCarthy, Minsky and others come together to study artificial intelligence. 1958 McCarthy defined the high-level language Lisp, • Lisp was to become the dominant AI programming language 1965 Robinson’s discovery of the resolution method • a complete theorem-proving algorithm for first-order logic History of Artificial Intelligence 1966-74 AI d i scovers computa t iona l complexity • Neural network research almost disappears 1969-79 Early development of knowledge- based systems 1980-88 Expert systems industry booms 
 1985-95 Neural networks return to popularity 
 1987- AI adopts the scientific method • Hidden Markov Models (HMMs), Bayesian network formalism for uncertainty, data mining 2001- Availability of very large data sets The State of The Art • What can AI do today? • Robotic vehicles: driverless robotic cars 
 • Speech recognition 
 • Autonomous planning and scheduling: NASA’s Remote Agent program became the first on-board autonomous planning program to control the scheduling of operations for a spacecraft 
 • Game playing: IBM’s DEEP BLUE became the first computer program to defeat the world champion in a chess match • Spam fighting • Robotics • Machine Translation