1. Artificial Intelligence CAP492
Dr. Souham Meshoul
Information Technology Department
CCIS – King Saud University
Riyadh, Saudi Arabia
CAP 492 Course Dr. Souham Meshoul

2. INTRODUCTION TO ARTIFICIAL INTELLIGENCE Chapter 1
CAP 492 Course Dr. Souham Meshoul

3. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Goal of Artificial Intelligence: Not only to understand how does mind work? but also how to build intelligent entities?.
Engineering point of view: -Solve real-world problems using knowledge and reasoning
-Develop concepts, theory and practice of building intelligent entities
- Emphasis on system building
Scientific point of view: Use computers as a platform for studying intelligence itself
- Emphasis on understanding intelligent behavior.
Artificial Intelligence is one of the newest sciences which emerged after the world war II. AI represents a big and open field.
The name Artificial Intelligence was adopted for the first time in (Computational Intelligence)
Artificial Intelligence can be viewed as a universal field: Ho to automate intellectual tasks?
CAP 492 Course Dr. Souham Meshoul

4. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
What is artificial Intelligence?
Several definitions are available in the literature.
Thinking vs Behavior
Model humans vs Work from an ideal standard
Two points of views:
1. Thinking/Acting humanly: success is measured in term of fidelity to human performance.
2. Thinking/Acting rationally: success is measured using an ideal concept of intelligence called
Rationality.
Rational System = system which does the « right thing » given what it knows.
CAP 492 Course Dr. Souham Meshoul

5. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Some AI Definitions:
According to thought processes and reasoning
Thinking like humans:
« The exciting new effort to make computers think…machines with minds, in the full and literal sense. » (Haugeland, 1985).
« The automation of activities that we associate with human thinking, activities such as decision-making, problem solving, learning… » (bellman, 1978).
Thinking rationally:
« The study of mental faculties through the use of computational models. » (Charniak and Mcdermott, 1985).
« The study of the computations that make it possible to perceive, reason and act. » (Winston 1992).
CAP 492 Course Dr. Souham Meshoul

6. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Some AI Definitions:
According to behavior
Acting Humanly
« The art of creating machines that perform functions that require intelligence when performed by people. » (Kurzweil, 1990)
« The study of how to make computers do things at which, at the moment, people are better. » (Rich and Knight, 1991)
Acting rationally
« Computational intelligence is the study of the design of intelligent agents. » (Poole et al.,1998).
« AI…is concerned with intelligent behavior in artifacts. » (Nilsson, 1998).
CAP 492 Course Dr. Souham Meshoul

7. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Some other definitions
Weak AI: (Searle) AI develops useful, powerful applications
Strong AI: claims machines have cognitive minds comparable to humans
debates on strong AI bring up some difficult conceptual problems in philosophy..
Keep this one : Science of making machines do things that would require intelligence if done by humans Marvin Minsky
CAP 492 Course Dr. Souham Meshoul

8. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Acting Humanly
Turing Test: Alan Turing « Computing Machinery and Intelligence »
CAP 492 Course Dr. Souham Meshoul

9. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Test: The computer passes the “test of intelligence” if a human, after posing some written questions, cannot tell whether the responses were from a person or not.
Provides an operational definition of AI.
Still being used today within the AI community in annual competitions – the Loebner Prize ($100,000 and a true 18K gold medal)
To give an answer, the computer would need to posses some capabilities:
Natural language processing: To communicate successfully.
Knowledge representation: To store what it knows or hears.
Automated reasoning: to answer questions and draw conclusions using stored information.
Machine learning: To adapt to new circumstances and to detect and extrapolate patterns.
Computer vision: To perceive objects.
Robotics to manipulate objects and move.
CAP 492 Course Dr. Souham Meshoul

10. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Example: ELIZA program, some extracts from the test:
Patient: Men are all alike.
ELIZA: In what way?
Patient: They are always bugging about something or the other.
ELIZA: Can you think of a specific example?
Patient: Well, my boyfriend made me come here.
ELIZA: Your boyfriend made you come here?
Patient: He says I’m depressed much of the time.
ELIZA: I am sorry you are depressed.
Problems:
ELIZA and similar programs stressed simple syntactic analysis and generation of sentences. They used pattern matching with known sentences.
Turing’s test is not reproducible or amenable to mathematical analysis (it suggests duplicate an examplar).
An alternative: Study the underlying principles of intelligence (Wright brothers)
CAP 492 Course Dr. Souham Meshoul

11. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Thinking Humanly:
Program think like human → How humans think?
Requires Scientific theories of internal activities of the brain (cognitive science and cognitive neuroscience).
Example:
The General Problem Solver (GPS designed by Newell and Simon In 1963) was meant to be a program that simulated human thought.
GPS: used means-end analysis in its search for solutions, computing the difference between the goal and current, and then attempting to minimize the difference.
Newell and Simon by comparing GPS traces with those of human subjects discovered that the behavior of GPS was largely a subset of human behavior
CAP 492 Course Dr. Souham Meshoul

12. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Thinking rationally:
The Laws of Thought approach is based on pattern for argument structure arising from Aristostle’s syllogisms.
Example, “Socrates is a man; all men are mortal, therefore Socrates is mortal.” The laws of thought initiated the field of logic.
The formal logic movement was advanced by Peano, Boole, Frege,, Godell and others (late 1800’s and early 1900’s)
Inspired perhaps by early progress, Hibert became a proponent of a school of thought known as logicism or formalism. The goal of this was to devise a logic, or formal system, capable of deriving all mathematical theorems.
CAP 492 Course Dr. Souham Meshoul

13. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Acting rationally:
Modern AI can be characterized as the engineering of rational agents.
An agent is simply an entity that perceives and acts. A rational agent is an entity that perceives, reasons and acts rationally (correctly).
CAP 492 Course Dr. Souham Meshoul

14. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Foundations:
An interdisciplinary subject found on:
Philosophy,
mathematics,
economics,
neuroscience,
psychology,
computer engineering,
linguistics, and so on
CAP 492 Course Dr. Souham Meshoul

15. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
History of Artificial Intelligence
Big dream
Ultimately, we are dealing with the question: “What are we (human beings) doing when we are thinking?”
Thought processes in the human mind are computational in nature. There are mechanistic procedures for generating these thoughts.
Such computations can be simulated and implemented by a Turing machine. Therefore, it can be programmed.
CAP 492 Course Dr. Souham Meshoul

16. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
History of Artificial Intelligence
Early days ( )
1943: first piece of AI work: Warren McCulloch and Walter Pitts
Model of artificial neurons
Mathematical learnable functions that generate “on/off” depending on inputs (logic gates)
Any computable function can be computed by a network of connected neurons.
Suitably defined networks can learn.
1949: Hebbian learning
A mechanism for updating the connection strength of a neuron.
Today, neurologists have confirmed that something similar to Hebbian learning indeed is going on in our brain when we are learning.
1950: Turing test, complete vision of AI in “computing machinery and Intelligence”
1951: first neural network computer
Implemented by M. Minsky and D. Edmonds
CAP 492 Course Dr. Souham Meshoul

17. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
History of Artificial Intelligence
Early days ( ): Mcculloch and pitts artificial neuron
0.3 1 -1  1
0.5
CAP 492 Course Dr. Souham Meshoul

18. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
History of Artificial Intelligence
Birth of AI 1956
1956: Dartmouth Conference
Organized by John McCarthy and colleagues for starting a new area in studying computation and intelligence.
John McCarthy introduced the term “artificial intelligence” in the conference.
The next 20 years witnessed steady growth of the field led by the pioneers appeared in the Dartmouth conference.
CAP 492 Course Dr. Souham Meshoul

19. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
History of Artificial Intelligence
Expectations and Initial enthusiasm (1952 – 1969)
1956: Samuel’s checkers program:
First game playing program achieving human-competitive performance.
1957: Simon’s general problem solver (GPS):
Imitates the way a human would solve planning problems.
1958: Invention of LISP by J. McCarthy.
The first AI programming language.
1958: Minsky’s microworlds
The concept of creating a “controlled environment” in which problem solving appears to require intelligence was born. The study of computation and intelligence can become more manageable in these micro-worlds
CAP 492 Course Dr. Souham Meshoul

20. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Expectations and Initial enthusiasm (1952 – 1969)
1963: Thomas Evan’s program ANALOG
Solved analogy problems in an IQ test.
1965: ELIZA
Simulates a dialog with a computer in English on any topic.
Became popular when programmed to simulate a psychotherapist (Fedora’s Emacs).
1967: Dendral program (developed at Stanford)
First successful program for scientific reasoning – one of the earlier rule based expert systems. A program that can infer molecular structures given the information provided by a mass spectrometer (that gives the masses of the various fragments of a molecule). The program relies on expert knowledge (encoded as rules) to constraint the generation of possible molecular structures that are consistent with the information from the mass spectrometer
CAP 492 Course Dr. Souham Meshoul

21. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
History of Artificial Intelligence
Reality Check (1966 – 1973): series of disappointments and frustrations
AI was poured little buckets of “reality cold water”
Problems
Most early systems contain little or no knowledge of their subject matter
Knowledge acquisition bottleneck.
Example: Poor performance of earlier machine translation system (Russian  English): “the spirit is willing but the flesh is weak” was translated to “the vodka is good but the meat is rotten”.
Computational Intractability of AI problems
Theory of computational complexity was not developed. Polynomial solvable problems, NP-completeness, etc
People thought a faster machine could solve any hard problem.
Initial frustration with theorem proving led to a disappointment in AI. Theorem proving is exponential in complexity
CAP 492 Course Dr. Souham Meshoul

22. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
History of Artificial Intelligence
Resurgence (1969 – 1979)
1971: T. Winograd’s Ph.D. thesis (MIT) demonstrated a system that can understand English in a micro-domain (the block world).
1972: PROLOG was developed by a group of Europeans and became alternative to LISP as an AI programming language.
1974: MYCIN was developed by Ted Shortliffe. Expert system for medical diagnosis. Sometimes called the first expert system.
1978: The Version Space algorithm was developed by Tom Mitchell at Stanford. First symbolic machine learning algorithm. “Father of Machine Learning”.
1979: Non-monotonic logic. Began to be formalized by John McCarthy and his colleagues.
CAP 492 Course Dr. Souham Meshoul

23. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
History of Artificial Intelligence
Resurgence (1969 – 1979): Winograd 1972
CAP 492 Course Dr. Souham Meshoul

24. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
History of Artificial Intelligence
AI becomes an industry (1980 – present)
AI started to become industrially and commercially beneficial
1982: R1 was deployed at DEC – an expert system that saved the company around $40M / year
Du Pont had 100 in use and an estimated 500 in development at late 90’s to early 21st century
At an international level, AI was considered a part of a country’s technological developments
Japan: “First Generation” project (10 year plan to build intelligence machines running in Prolog)
USA: Microelectronics and Computer Technology Corporation (MCC) was formed in response
Britain: Funding for AI was reinstated
CAP 492 Course Dr. Souham Meshoul

25. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
History of Artificial Intelligence
Renewing with connectionism and AI becomes a science (1986 – present)
Work of the physicist John Hopfield (1982) on using techniques from statistical mechanics.
Connectionist models of intelligent systems competitor to the symbolic models (Newell and Simon) and logicist approach (McCarthy). (complementary approaches in fact).
Several revolutions in many fields: pattern recognition, computer vision, robotics…
Emergence of intelligent agents.
CAP 492 Course Dr. Souham Meshoul

26. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Examples of AI applications: Game Playing
TDGammon, the world champion backgammon player, built by Gerry Tesauro of IBM research.
Perception: keyboard input.
Reason: reinforcement learning.
Actuation: graphical output shows dice and movement of piece.
Deep Blue chess program beat world champion Gary Kasparov
Perception: input symptoms and test results.
Reason: Bayesian networks, Monte-Carlo simulations.
Actuation: output diagnoses and further test suggestions.
CAP 492 Course Dr. Souham Meshoul

27. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Examples of AI applications: Natural Language Understanding
Natural language understanding (spell checkers, grammar checkers)
AI translators – spoken to and prints what one wants in foreign languages : Alta Vista’s translation of web pages.
Advanced systems can answer questions based on the information in the text and produce useful summaries.
PROVERB (Littman 1999) crossword puzzles
Examples of successes : English conversation
START system: accesses raw data tables, and then can carry on a dialogue
CAP 492 Course Dr. Souham Meshoul

28. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Examples of AI applications: Expert systems
In geology
prospector expert system carries evaluation of mineral potential of geological site or region
Diagnostic Systems
Pathfinder, a medical diagnosis system (suggests tests and makes diagnosis) developed by Heckerman and other Microsoft research
Microsoft Office Assistant in Office provides customized help by decision-theoretic reasoning by an individual user.
MYCIN system for diagnosing bacterial infections of the blood and suggesting treatments
System Configuration
"XCON" (for custom hardware configuration) configures computers doing work of 300 people using 10,000 rules
CAP 492 Course Dr. Souham Meshoul

29. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Examples of AI applications: Robotics
Robotics becoming increasing important in various areas like: games, to handle hazardous conditions and to do tedious jobs among other things.
Examples: automated cars, ping pong player, mining, construction, robot assistant in microsurgery,…
CAP 492 Course Dr. Souham Meshoul

30. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Main issues in AI
Representation
Search: many tasks can be viewed as searching a very large problem space for solution space
Inference: related to search, inferring other facts from some given facts. e.g., knowing all “elephants have trunks” and “Jo is an elephant,” can we answer does Jo have a trunk?
Learning: inductive inference, neural networks, artificial life, genetic algorithms, evolutionary strategies
Planning: starting with general facts about the world, facts about the effects of basic actions, facts about a particular situation, and a statement of a goal, generate a strategy for achieving that goal in terms of a sequence of primitive steps or actions
CAP 492 Course Dr. Souham Meshoul

31. INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Summary
Intelligence is studied from many perspectives: Are you concerned with thinking or behavior?
AI can help us solve difficult, real-world problems, creating new opportunities in business, engineering, and many other application areas.
The history of AI has had cycles of success, misplaced optimism, and resulting cutbacks in enthusiasm and funding. There have also been cycles of introducing new creative approaches and systematically refining the best ones.
AI has advanced more rapidly in the past decade because of greater use of the scientific method in experimenting with and comparing approaches.
CAP 492 Course Dr. Souham Meshoul