Lec1 introduction
- 1. Artificial Intelligence Lecturere: Sheheen A. Abdulkareem Dept. of Computer Science Faculty of Science University of Dohuk Sep, 2010
- 2. What is Intelligence? • Simply, it defined as set of properties of the mind! • The properties include the ability to plan, solve problems, and reason. • Simpler, is the ability to make right decision given a set of inputs and variety of possible action • The ability to learn or understand or to deal with new or trying situations! • The ability to apply knowledge to manipulate one's environment or to think abstractly as measured by objective criteria (as tests)!
- 3. What is AI? • Not just making Computers, Robots, or agents acts like humans! – They should think like humans not like machines! – We don’t want them to make humans mistakes! – We want them to learn but not from the time 0! • The key is the ability to “share” learned results (i.e. copy data/program) between computers.
- 4. Hmm!
- 5. So… • AI is not just studying intelligent systems, but building them… • Psychological approach: an intelligent system is a model of human intelligence! • Engineering approach: an intelligent system solves a sufficiently difficult problem in a generalizable way!
- 6. The AI Semester Objectives • Become familiar with AI techniques, including their implementations – be able to develop AI applications using Python! • Understand the theory behind the techniques, knowing which techniques to apply when (and why) • Become familiar with a range of applications of AI – We will focus on Agent-based Modelling and applying it using NetLogo software!
- 7. AI History? • Gestation (the early 1950’s): – McCulloch and Pitts artificial neuron, Hebbian learning – Early learning theory, first neural network, Turing test • Birth (1957): – The Logic Theorist – Name coined by McCarthy – Workshop at Dartmouth
- 8. Cont’d… • Early enthusiasm, great expectations (1952- 1969) – GPS, physical symbol system hypothesis – Geometry Theorem Prover (Gelertner), Checkers (Samuels) – Lisp (McCarthy), Theorem Proving (McCarthy), Microworlds (Minsky et. al.) – “neat” (McCarthy @ Stanford) vs. “scruffy” (Minsky @ MIT)
- 9. Cont’d… • Dose of Reality (1966-1973) – Combinatorial explosion • Knowledge-based systems (1969-1979) • AI Becomes an Industry (1980-present) – Boom period 1980-88, then AI Winter • Return of Neural Networks (1986-present) • AI Becomes a Science (1987-present) – SOAR, Internet as a domain
- 10. What is AI (Again)? • Systems that think like • Systems that think humans! rationally! • Cognitive Modeling Approach – Laws of Thought approach • The automation of activities – The study of mental faculties that we associate with human through the use of computational thinking... models. • Systems that act like • Systems that act rationally! humans! • Rational Agent Approach • Turing Test Approach • The branch of CS that is • The art of creating machines concerned with the automation of that perform functions that intelligent behavior require intelligence when performed by people
- 11. Acting Humanly! • Turning Machine: Introducing the concept of his universal abstract machine. – Simple and could solve any mathematical problem. • Turning test: if the machine could fool a human into thinking that it was also human, then it passed the intelligence test. Can Machines Think?
- 12. Acting Humanly, Cont’d… • Operational test for intelligent behavior • The Imitation Game • Problem! – The turning test is not reproducible, constructive, or amenable to mathematical analysis
- 13. Thinking Humanly! • 1960’s cognitive revolution • Requires scientific theories of internal activities of the brain • What level of abstraction? “Knowledge” or “Circuits” • How to validate? – Predicting and testing behavior of human subjects (top-down) – Direct identification from neurological data (bottom-up) • Cognitive Science and Cognitive Neuroscience • Now distinct from AI
- 14. Thinking Rationally • Normative (or prescriptive) rather than descriptive • Aristotle: What are correct arguments / thought processes? • Logic notation and rules for derivation for thoughts • Problems: • Not all intelligent behavior is mediated by logical deliberation • What is the purpose of thinking? What thoughts should I have?
- 15. Acting Rationally • Rational behavior • Doing the right thing • What is the “right thing”? • That which is expected to maximize goal achievement, given available information • We do many (“right”) things without thinking • Thinking should be in the service of rational action
- 16. Applied Areas of AI • Heuristic Search • Computer Vision • Adversarial Search (Games) • Fuzzy Logic • Natural Language Processing • Knowledge Representation • Planning • Learning
- 17. Concepts to be learned • Problem-Solving – Uninformed Search – Informed Search • AI and Games • Machine Learning • Evolutionary Computation • Robotics and AI • Intelligent Agents and Agent-based Modeling
- 18. Semester Tools • References as Textbooks: – Artificial Intelligence a system approach, by M. Tim Jones, 2008. – Artificial Intelligence A modern Approach, 3rd Edition, by Stuart J. Russell and Peter Norving, 2010.