1. INFO 372: Explorations in Artificial Intelligence
Prof. Carla P. Gomes
Introduction

2. Overview of this Lecture
Course Administration
What is Artificial Intelligence?
Course Themes, Goals, and Syllabus

3. Course Administration

4. Artificial Intelligence
INFO372 – Explorations in
Artificial Intelligence
Lectures: Tuesday and Thursday - 10: :25
Location: Olin Hall, room 216
Lecturer: Prof. Gomes
Office: 5133 Upson Hall
Phone:
Administrative Assistant: Beth Howard
    5136 Upson Hall,
TA: Robert Xiao
Web Site:

5. Office Hours TA: Robert Xiao (TBA) Prof. Gomes:
Office: 5133 Upson Hall
I prefer to meet during my scheduled office hours,
however, if you need to meet with me at a different
time please schedule an appointment by .
Wednesdays: 2:30p.m – 3:30 p.m.

6. Grades Midterm (25%) Homework (30%) Participation (5%) Final (40%)
Note: The lowest homework grade will be dropped before the
final grade is computed.

7. Homework
Homework is very important. It is the best way for you to learn the material.
Your lowest homework grade will be dropped before the final grade is computed.
You are encouraged to discuss the problems with your classmates, but all work handed in should be original, written by you in your own words.
No late homework will be accepted

8. Textbook Linear Programming by Vasek Chvatal
Artificial Intelligence: A Modern Approach (AIMA)
(Second Edition) by Stuart Russell and Peter Norvig
Artificial Intelligence : A New Synthesis
By Nils Nilsson
Principles of Constraint Programming
By Krzysztof Apt
Linear Programming by Vasek Chvatal

9. Overview of this Lecture
Course Administration
What is Artificial Intelligence?
Course Themes, Goals, and Syllabus

10. What is Artificial Intelligence (AI)?
What is Intelligence? Historical Perspective of AI State-of-the-art and Challenges

11. What is AI? Ambitious goals: understand “intelligent” behavior
build “intelligent” agents

12. What is Intelligence? Intelligence:
“the capacity to learn and solve problems”
(Webster dictionary)
the ability to act rationally
Artificial Intelligence:
build and understand intelligent entities
synergy between:
philosophy, psychology, and cognitive science
computer science and engineering
mathematics and physics

13. AI Leverages from Different Disciplines
Philosophy
e.g., foundational issues in logic, methods of reasoning,
mind as physical system, foundations of learning,
language, rationality
Computer science and engineering
e.g., complexity theory, algorithms, logic and inference,
programming languages, and system building (hardware
and software).
Mathematics and physics
e.g., statistical modeling, continuous mathematics, Markov
models, statistical physics, and complex systems.
and others, e.g., cognitive science, neuroscience, economics, psychology, linguistics, …

14. AI: Historical Perspective
Obtaining an understanding of the human mind is one of the
final frontiers of modern science.
Founders:
George Boole, Gottlob Frege, and Alfred Tarski
formalizing the laws of human thought
Alan Turing, John von Neumann, and Claude Shannon
thinking as computation
John McCarthy, Marvin Minsky, Herbert Simon, and Allen Newell
the start of the field of AI (1959)

15. In 1936, Alan Turing, a British mathematician, showed that there exists a relatively simple universal computing device that can perform any computational process.
Computers use such a universal model.
Alan Turing
Turing Machine
(abstraction)
Turing also showed the limits of computation – some problems cannot be computed even with the most powerful computer and even with unlimited amount of time – e.g., Halting problem.

16. Acting humanly: Turing Test
Alan Turing
Turing (1950) "Computing machinery and intelligence":
"Can machines think?"  "Can machines behave intelligently?"
Operational test for intelligent behavior: the Imitation Game
Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes
Anticipated all major arguments against AI in following 50 years
Suggested major components of AI: knowledge, reasoning, natural language understanding, learning
AI system passes
if interrogator
cannot tell which one
is the machine

17. Some Famous Imitation Games
1960s ELIZA Joseph Weizenbaum
Rogerian psychotherapist
1990s ALICE
Loebner prize
win $100,000 if you pass the test

18. ELIZA: impersonating a Rogerian psychotherapist
1960s ELIZA Joseph Weizenbaum
Eliza:
a friend you could never have before
Eliza: Hello. I am ELIZA. How can I help you?
You: Well I feel sad
Eliza: Do you often feel sad?
You: not very often, but it's becoming more common
Eliza: Please go on.

19. Chat Bot – ALICE (AI Foundation)
Human: hi my name is Carla ALICE: How are you doing, Carla. Human: what is your name? ALICE: My name is ALICE. Human: what is your full name? ALICE: ALICE = Artificial Linguistic Internet Computer Entity. Human: when were you born? ALICE: I was connected to the net on November 23, 1995.
Human: Have you passed the Loebner Prize? ALICE: The Loebner Prize is an annual Turing Test sponsored by New York philanthropist Hugh Loebner. Dr. Wallace and ALICE won it in The ALICE program was ranked "most human computer" by the panel of judges.
Human: who is Dr. Wallace? ALICE: He is a famous computer scientist, the author of ALICE, and a graduate of Carnegie Mellon.
Human: Where does Dr. Wallace work? ALICE: he works from his home in San Francisco.

20. Acting humanly: Turing Test
Turing test  identified key research areas in AI:
Natural Language Processing – to communicate with the machine;
Knowledge Representation – to store and manipulate information;
Automated reasoning – to use the stored information to answer questions and draw new conclusions;
Machine Learning – to adapt to new circumstances and to detect and extrapolate patterns.
but does a machine need to act humanly
to be considered intelligent?

21. Other perspectives on Intelligence
Thinking humanly: cognitive modeling
Requires scientific theories of internal activities of the brain; How to validate?
1) Cognitive Science (top-down)  Predicting and testing behavior of human subjects
– computer models + experimental techniques from psychology
2) Cognitive Neuroscience (bottom-up)  Direct identification from neurological data
Thinking rationally: "laws of thought“
Logic  Making the right inferences! Several Greek schools developed various forms of logic: notation and rules of derivation for thoughts;
Aristotle: what are correct arguments/thought processes? (characterization of “right thinking”);
Socrates is a man
All men are mortal
Therefore Socrates is mortal
More contemporary logicians (e.g. Boole, Frege, Tarski)  Direct line through mathematics and philosophy to modern AI
Acting rationally: rational agent
Rational behavior: doing the right thing; that which is expected to maximize goal achievement, given the available information; Doesn't necessarily involve thinking – e.g., blinking reflex – but thinking should be in the service of rational action;
Always doing the right thing  sometimes not feasible in complex environments  Computational demands can be too high!

22. What is AI? Thinking humanly Thinking Rationally Acting Humanly
Human-like
Intelligence
“Ideal” Intelligent/
Rationally
Thinking humanly
Thinking
Rationally
Acting
Humanly
Thought/
Reasoning
A system is intelligent if it does the right thing given what it knows.
Behavior/
Actions

23. Different Approaches I Building exact models of human cognition
view from psychology and cognitive science
II Developing methods to match or exceed human
performance in certain domains, possibly by
very different means  e.g., Deep Blue;
Focus of INFO372 (most recent progress).

24. What's involved in Intelligence?
A) Ability to interact with the real world
to perceive, understand, and act
speech recognition and understanding
image understanding (computer vision)
B) Reasoning and Planning
modelling the external world
problem solving, planning, and decision making
ability to deal with unexpected problems, uncertainties
C) Learning and Adaptation
We are continuously learning and adapting.
We want systems that adapt to us!
INFO 372

25. Reasoning and Planning in AI
State-of-the-art
Reasoning and Planning in AI
A few examples…

26. Robbin’s Algebras are all boolean
EQP:
Robbin’s Algebras are all boolean
A mathematical conjecture (Robbins conjecture) unsolved for decades
The Robbins problem was to determine whether one particular set of rules is powerful enough to capture all of the laws of Boolean algebra. One way to state the Robbins problem in mathematical terms is:
Can the equation not(not(P))=P be derived from the following three equations?
[1] P or Q = Q or P,
[2] (P or Q) or R = P or (Q or R),
[3] not(not(P or Q) or not(P or not(Q))) = P.
[An Argonne lab program] has come up with a major mathematical
proof that would have been called creative if a human had thought of it.
New York Times, December, 1996

27. Deep Blue beats the World Chess Champion
1997:
Deep Blue beats the World Chess Champion
vs.
I could feel human-level intelligence across the room
-Gary Kasparov, World Chess Champion (human…)

28. Deep Blue vs. Kasparov One of the most famous modern computers,
Game 1: 5/3/97: Kasparov wins  
Game 2: 5/4/97: Deep Blue wins    
Game 3: 5/6/97: Draw     
Game 4: 5/7/97: Draw     
Game 5: 5/10/97: Draw     
Game 6: 5/11/97: Deep Blue wins     
“I felt a new kind of
Intelligence” ( across
the board from him)
Kasparov 1997
The value of IBM’s stock
Increased by $18 Billion!
One of the most famous modern computers,
Deep Blue, which defeated Gary Kasparov at chess.

29. 1999: Remote Agent takes Deep Space 1 on a galactic ride
For two days in May, 1999, an AI Program called Remote Agent
autonomously ran Deep Space 1 (some 60,000,000 miles from earth)

30. Remote Agent: 1999 Winner of NASA's Software of the Year Award
It's one small step in the history of space flight. But it was one giant leap for computer-kind, with a state of the art artificial intelligence system
being given primary command of a spacecraft. Known as Remote Agent,
the software operated NASA's Deep Space 1 spacecraft and its futuristic ion engine during two experiments that started on Monday, May 17, 1999.
For two days Remote Agent ran on the on-board computer of Deep Space 1,
more than 60,000,000 miles (96,500,000 kilometers) from Earth.
The tests were a step toward robotic explorers of the 21st century that are
less costly, more capable and more independent from ground control.
A hundred million miles from Earth, NASA’s Remote Agent program became the first on-board autonomous planning program to control the scheduling of operations of the spacecraft.
Remote Agent generated plans from high-level goals specified from the ground, and it monitored the operation of the spacecraft as the plans were executed – detecting, diagnosing, and recovering from problems ass they occurred

31. 2000: SCIFINANCE synthesizes programs for financial modeling
Develop pricing models for complex derivative structures
Involves the solution of a set of PDEs (partial differential equations)
Integration of object-oriented design, symbolic algebra, and plan-based scheduling

32. better than most humans
Proverb 1999: Solving Crossword Puzzles as Probabilistic Constraint Satisfaction
Proverb solves
crossword puzzles
better than most humans
Large database of past puzzles, a variety of information sources, including dictionaries and thesaurus, and online databases
such as list of movies and actors, etc
Michael Littman et a. 99

33. Cornell 199

34. 2003 Robocup Italy

35. 2005 Autonomous Control: DARPA GRAND CHALLENGE
October 9, 2005
Stanley and the Stanford RacingTeam
were awarded 2 million dollars for being the
first team to complete the 132 mile
DARPA Grand Challenge course (Mojave Desert).
Stanley finished in just under 6 hours 54 minutes
and averaged over 19 miles per hours on the course.

38. Course Themes, Goals, and Syllabus

39. Goals of INFO 372 Formalisms: Logical representations;
Introduce the students to a range of computational modeling
approaches and solution strategies using examples from AI and
Information Science.
Formalisms:
Logical representations;
Constraint-based languages,
Mathematical programming;
Multi-agent formalisms (including adversarial games);
Solution strategies:
Logical inference;
General complete backtrack search;
Local search;
Dynamic Programming;

40. Goals of INFO 372 Special models:
Satisfiability (SAT); Maximum SAT; Horn
Constraint Satisfaction; Binary Constraint Satisfaction;
Mixed Integer Programming, Linear Programming and
Network Flow Models;
Themes:
Expressiveness and efficiency tradeoffs of the various representation formalisms
Students learn about the tradeoffs in modeling choices.;
Concrete examples to move from one representation modeling formalism to another formalism;

41. Summary
Discussed Artificial Intelligence and characteristics of intelligent systems.
Gave series of example systems, involving e.g.
game playing, automated reasoning, and planning.
Computers are getting smarter !!!
Suggested Reading: Chapter 1 Russell & Norvig

42. The END