1. Introduction1 Artificial Intelligence (AI) Introduction Chapter 1

2. Introduction2 Outline of this Chapter What is Intelligence? What is Artificial Intelligence ( AI )? Definitions of AI The state of the art

3. Introduction3 Why AI is exciting? To learn more about ourselves. Build intelligent entities & understand them Computers with human level of intelligence  would have an impact on our everyday lives. How is it possible for a tiny brain to: 1.Perceive (To become aware ) 2.Understand 3.Predict (Anticipate ) and Communicate ( Manipulate a world larger & complicated than itself.)

4. Introduction4 What is Intelligence? Most of the people believe that intelligence is the ability to grasp the essentials in any situation and to response appropriately OR A person is said to be intelligent if he can judge the situation from surface that what is happening inside A person is said to be intelligent if he can give suitable answer of the question more than others, this may not be considered as the criteria for the evaluation of intelligence because the reason of the person’s response for any question depends upon the person’s knowledge of context. George Wilson says “ If intelligence means quick response then an author can hardly afford to be intelligent” Different people show their intelligence in different ways –Some people whose ability to deduce conclusion is very high but they are not good in expressing their ideas in good worded manner –Some people are good authors, their writing power helps to entertain and persuade the people but they are not good in other concepts e.g. politics

5. Introduction5 What is Intelligence? To be intelligent in a particular field a person should have knowledge about that field, also his processing speed should be so fast to retrieve that knowledge in limited time Patrick Winston said about intelligence that –It is the ability to reason –It is the ability to acquire and apply the knowledge –It is the ability to communicate and manipulate ideas Indeed definition of intelligence is impossible appropriately

6. Introduction6 What is AI? AI is a broad area –means different things to different people –making computers do things that require (human) intelligence Why make computers do these things? –To automate difficult tasks –To create smart programs –To understand intelligence better Definitions of AI fall into four categories –Systems that act like humans- e.g. ELIZA, Turing Test. –Systems that think like humans (cognitive science) –Systems that think rationally (do the right thing) –Systems that act rationally (The textbook supports this def.)

7. Introduction7 What is AI? (cont.) Views of AI fall into four categories: Definitions of artificial intelligence according to eight textbooks are shown in Figure 1.1 p.2. These definitions vary along two dimensions. The ones on top are concerned with thought processes and reasoning, whereas the ones on the bottom address behavior. The definitions on the left measure success in terms of loyalty to human performance, whereas the ones on the right measure against an ideal concept of intelligence which we call rationality A system is rational  if it does the “right thing”, given what it knows. The textbook supports "acting rationally” Thinking humanly Thinking rationally Acting humanly Acting rationally top bottom leftright

8. Introduction8 What is AI? (cont.) Historically, all 4 approaches to AI have been followed. As one might expect, a tension exists between approaches centered around humans and approaches centered around rationality. A human-centered approach must be an empirical science, involving hypothesis (Guess) and experimental confirmation. A rationalist approach involves a combination of mathematics and engineering. Each group has both criticized and helped the other. Let us look at the four approaches in more detail.

9. Introduction9 Acting humanly: Turing Test "Can machines think?"  "Can machines behave intelligently?“ A machine that passes the test should certainly be considered as intelligent. Ability to achieve human-level performance in all cognitive tasks. Computer should be interrogated by a human via a terminal The Turing test, proposed by Alan Turing (1950), discussed conditions for considering a machine to be intelligent. machine could successfully pretend to be human to a knowledgeable observer then should consider it intelligent. "the imitation game" in his 1950 article Computing Machinery and Intelligence (Mind, Vol. 59, No. 236, pp. 433-460) Computing Machinery and Intelligence (Mind, Vol. 59, No. 236, pp. 433-460)

10. Introduction10 Turing test in the dictionary means Turing test in Computing a test for intelligence in a computer, which requires that a human should be unable to distinguish it from another human by the replies to questions put to both.

11. Introduction11 Acting humanly (Cont.) –If the machine can fool an interrogator, it is intelligent. The computer requires the following capabilities: Natural Language processing to enable it to communicate successfully in English Knowledge representationto store what it knows or hears Automated reasoningto use the stored information to answer questions & to draw new conclusions. Machine learningto adapt to new circumstances & to detect patterns. Turing’s test deliberately avoided physical interaction between the interrogator and the computer, because physical simulation of a person is unnecessary for intelligence. To pass the total Turing Test, the computer will need: Computer vision  to perceive objects, and Robotics  to manipulate objects. ELIZA: A program that communicates in natural language, simulates a psychotherapist interacting with a patient and successfully passed the Turing Test.

12. Introduction12 Acting humanly (Cont.) These 6 disciplines compose most of AI 1.Natural Language processing 2.Knowledge representation 3.Automated reasoning 4.Machine learning 5.Computer vision 6.Robotics And Turing deserves credit for designing a test that remains relevant 50 years later. Yet AI researchers have devoted little effort to passing the Turing test, believing that it is more important to study the underlying principles of intelligence than to duplicate an exemplar.

13. Introduction13 Thinking humanly: cognitive modeling If we are going to say that a given Program thinks like a human so we need to determine how humans think? We need to get inside the actual workings of human minds. There are two ways to do this: through introspection: trying to catch our own thoughts as they go by & through psychological experiments. Scientific theories of internal activities of the brain are required Once we have a sufficiently precise theory of the mind  it becomes possible to express the theory  as a computer program. If the program’s input/output and timing behaviors match corresponding human behaviors  that is evidence that some of the program’s mechanisms could also be operating in humans.

14. Introduction14 Cognitive science approach focus not just on behaviour, but look at reasoning process. Allen Newell and Herbert Simon, who developed GPS, the “General Problem Solver” 1961, were not content to have their program solve problems correctly. They were more concerned with comparing: 1.the trace of its reasoning steps to 2.traces of human subjects solving the same problems. Computational model should reflect how results were obtained E.g. General Problem Solver (GPS), its goal not just to produce humanlike behaviour like ELIZA. But to produce a sequence of steps of the reasoning process that was similar to the steps followed by a person in solving the same task. The interdisciplinary field of cognitive science brings together: 1.computer models from AI and 2.experimental techniques from psychology  to try to construct precise and testable theories of the workings of the human mind. Thinking humanly: cognitive modeling

15. Introduction15 Thinking humanly: cognitive modeling (Summary) 1960s "cognitive revolution": information- processing psychology Requires scientific theories of internal activities of the brain -- How to validate? Requires 1) Predicting and testing behavior of human subjects (top-down) or 2) Direct identification from neurological data (bottom-up) Both approaches (roughly, Cognitive Science and Cognitive Neuroscience) are now distinct from AI

16. Introduction16 Thinking rationally: The laws of thought approach. Several Greek schools developed various forms of logic notation and rules of derivation for thoughts; may or may not have proceeded to the idea of mechanization. –Socrates is a man. All men are mortal. Therefore Socrates is mortal." – Aristotle. The goal is to formalize the reasoning process, producing a system that contains logical inference mechanisms that are provably correct, and guarantee finding an optimal solution. This study initiated the field of logic. –The logicist (solve any problem described in logical notation) tradition in AI hopes to create intelligent systems using logic programming. Problems to this approach –it is difficult to take a problem that is presented informally and transform it into the formal terms required by logical notation. This is particularly true when the knowledge you are representing is less than 100% certain. –solving even small problems (those with only a few initial facts) can be computationally expensive.

17. Introduction17 Acting rationally: rational agent Rational behavior: doing the right thing The “right thing” is that what is expected to maximize goal achievement, given the available information Doesn't necessarily involve thinking – e.g., blinking reflex. Yet rationality is only applicable in ideal environments. Moreover rationality is not a very good model of reality.

18. Introduction18 In this course We will focus mostly on rational systems "acting rationally”. A system is rational if it does the “right thing”, given what it knows. Our textbook supports “The rational agent approach” An agent is an entity that perceives and acts This course is about designing rational agents For any given class of environments and tasks, we seek the agent with the best performance Problem : computational limitations make perfect rationality unachievable  design best program for given machine resources

19. Introduction19 Foundations of AI AI is one of the newest fields since 1956, and oldest since 400 BC. It has inherited several ideas & techniques from many disciplines: Philosophyrules that can describe the working of the mind. mind as physical system  reasoning with the knowledge. How knowledge can be obtained from experience? connection between knowledge & action. Mathematicsprovided the tools to manipulate statements of logical & probabilistic statements and to understand computation & reasoning about algorithms. Neurosciencehow do brains process information? Psychology adopted the idea that human & animals can be considered information processing machines. Computer engineering makes AI application possible, building fast computers (large AI programs)

20. Introduction20 State of the art IBM’s Deep Blue became the first computer program to defeat the world chess champion Garry Kasparov in 1997 Proved a mathematical conjecture (Robbins conjecture) unsolved for decades Autonomous control: The ALVINN computer vision system was trained to steer a car to keep it following the lane. Driving autonomously 98% of the time from Pittsburgh to San Diego. During the 1991 Gulf War, US forces deploy an AI logistics planning and scheduling program that involved up to 50,000 vehicles, cargo, and people NASA's on-board autonomous planning program controlled the scheduling of operations for a spacecraft. Proverb is a computer program that solves crossword puzzles better than most humans. What can AI do today? A concise answer is difficult, because there are so many activities in so many subfields.

21. Introduction21 State of the art (cont.) AI techniques are used in many systems and applications, e.g.: speech recognition, video games, route planning, logistics planning, pharmaceutical drug design, diagnosis (medicine), road traffic monitoring, facial recognition, medical image analysis, language understanding & generation (translation, dialogue), etc...

22. Introduction22 Main Areas of AI Search, especially heuristic search (puzzles, games) Knowledge representation (including formal logic) Planning Reasoning with uncertainty, including probabilistic reasoning. Learning Agent architectures Robotics and perception Natural language processing Search Knowledge rep. Planning Reasoning Learning Agent Robotics Perception Natural language... Expert Systems Constraint satisfaction

23. Introduction23 Conclusion Different people think of AI differently. Adopt the rational action view: an intelligent agent takes the best possible action in a situation. AI has its root in many disciplines.