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CS-INFO 372: Explorations in Artificial IntelligencePowerPoint Presentation

CS-INFO 372: Explorations in Artificial Intelligence

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### CS-INFO 372:Explorations in Artificial Intelligence

### What is Intelligence?Historical Perspective of AIState-of-the-art and Challenges

Prof. Carla P. Gomes

Introduction

http://www.cs.cornell.edu/courses/cs372/2008sp

Artificial Intelligence

Course Administration

Lectures: Tuesday and Thursday - 10:10 - 11:25

Location: Phillips Hall, room 307

Lecturer: Prof. Gomes

Office: 5133 Upson Hall

Phone: 255 9189

Email: [email protected]

Administrative Assistant: Beth Howard

5136 Upson Hall, 255-4188

TAs: Robert Xiao [email protected]

Yunsong Guo <[email protected]>

Web Site:http://www.cs.cornell.edu/courses/cs372/2008sp

Office Hours

TAs:

Robert Xiao [email protected] TBA

Yunsong Guo [email protected] TBA

Prof. Gomes:

Office: 5133 Upson Hall

If you need to meet with me at a different time please

schedule an appointment by email.

Wednesdays 12:00 – 1:00 p.m.

Grades

Midterm (30%)

Homework (25%)

Participation (5%)

Final (40%)

Homework is very important. It is the best way for you to learn the material. 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

Textbook

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

Overview of this Lecture

- Course Administration
- What is Artificial Intelligence?
- Course Themes, Goals, and Syllabus

What is Artificial Intelligence (AI)?

What is Intelligence?

- Intelligence:
- “the capacity to learn and solve problems”
(Webster dictionary)

- the ability to act rationally

- “the capacity to learn and solve problems”
- Artificial Intelligence:
- build and understand intelligent entities
- synergy between:
philosophy, psychology, and cognitive science

computer science and engineering

mathematics and physics

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, statistics…

AI:Historical Perspective

Obtaining an understanding of the human mind is one of the

final frontiers of modern science.

Founders:

George Boole (1779-1848), Gottlob Frege (1848-1925), and Alfred Tarski (1902-1983)

formalizing the laws of human thought

Alan Turing (1912-1954) , John von Neumann (1903-1957), Claude Shannon (1916-2001)

thinking as computation

John McCarthy (1927- ), Marvin Minsky (1927 - ) , Herbert Simon (1916-2001), and Allen Newell (1927-1992)

the start of the field of AI (1959)

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.

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.

Halting Problem

- The halting problem is a decision problem which can be stated as follows:
- Given a description of a program and a finite input, decide whether the program finishes running or will run forever, given that input.

- Alan Turing proved in 1936 that a general algorithm to solve the halting problem for all possible program-input pairs cannot exist. We say that the halting problem is undecidable.

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

Some Famous Imitation Games

- 1960s ELIZA Joseph Weizenbaum
- Rogerian psychotherapist

- 1990s ALICE
- Loebner prize
- win $100,000 if you pass the test

ELIZA: impersonating a Rogerian psychotherapist

1960s ELIZA Joseph Weizenbaum

http://www-ai.ijs.si/eliza/eliza.html

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.

Chat Bot – ALICE(AI Foundation)

- Human: hi my name is CarlaALICE: 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 2000. 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.

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?

Focus of INFO372 (most recent progress).

Different ApproachesI 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;

Man vs. Machiens The Hardware

- The brain
- a neuron, or nerve cell, is the basic information processing unit (10^11 )
- many more synapses (10^14) connect the neurons
- cycle time: 10^(-3) seconds (1 millisecond)

- How complex can we make computers?
- 10^8 or more transistors per CPU
- supercomputer: hundreds of CPUs, 10^10 bits of RAM
- cycle times: order of 10^(-9) seconds (1 nanosecond)

- Conclusion
- In near future we can have computers with as many processing elements as our brain, but:
far fewer interconnections (wires or synapses)

much faster updates.

Fundamentally different hardware may require fundamentally different algorithms!

- Very much an open question.

- In near future we can have computers with as many processing elements as our brain, but:

What is AI?

Human-like

Intelligence

“Ideal” Intelligent/

Rationally

Thought/

Reasoning

Behavior/

Actions

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!

Deep Blue beats the World Chess Champion

vs.

I could feel human-level intelligence across the room

-Gary Kasparov, World Chess Champion (human…)

Deep Blue vs. Kasparov

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.

How Intelligent is Deep Blue?

- Saying Deep Blue doesn't really think about chess is like saying an airplane doesn't really fly because it doesn't flap its wings.
- Drew McDermott

On Game 2

(Game 2 - Deep Blue took an early lead.

Kasparov resigned, but it turned out he could

have forced a draw by perpetual check.)

This was real chess. This was a game any human

grandmaster would have been proud of.

Joel Benjamin grandmaster, member Deep Blue team

Kasparov on Deep Blue

- 1996: Kasparov Beats Deep Blue
“I could feel --- I could smell --- a new kind

of intelligence across the table.”

- 1997: Deep Blue Beats Kasparov
“Deep Blue hasn't proven anything.”

Game Tree Search

- How to search a game tree was independently invented by Shannon (1950) and Turing (1951).
- Technique called: MiniMax search.
- Evaluation function combines material & position.

History of Search Innovations

- Shannon, Turing Minimax search 1950
- Kotok/McCarthy Alpha-beta pruning 1966
- MacHack Transposition tables 1967
- Chess 3.0+ Iterative-deepening 1975
- Belle Special hardware 1978
- Cray Blitz Parallel search 1983
- Hitech Parallel evaluation 1985
- Deep Blue All of the above 1997

Transposition Tables

- Introduced by Greenblat's Mac Hack (1966)
- Basic idea: caching
- once a board is evaluated, save it in a hash table (data structure that associates keys with values), avoid re-evaluating.
- called “transposition” tables, because different orderings (transpositions) of the same set of moves can lead to the same board.
- Form of root learning (memorization)
- Don’t repeat blunders can’t beat the computer twice in a row using same moves

Deep Blue --- huge transposition tables (100,000,000+),

must be carefully managed.

Special-Purpose and Parallel Hardware

- Belle (Thompson 1978)
- Cray Blitz (1993)
- Hitech (1985)
- Deep Blue (1987-1996)
- Parallel evaluation: allows more complicated evaluation functions
- Hardest part: coordinating parallel search
- Deep Blue never quite plays the same game, because of “noise” in its hardware!

Deep Blue

- Hardware
- 32 general processors
- 220 VSLI chess chips

- Overall: 200,000,000 positions per second
- 5 minutes = depth14

- Selective extensions - search deeper at unstable positions
- down to depth 25 !

Tactics into Strategy

- As Deep Blue goes deeper and deeper into a position, it displays elements of strategic understanding. Somewhere out there mere tactics translate into strategy. This is the closest thing I've ever seen to computer intelligence. It's a very weird form of intelligence, but you can feel it. It feels like thinking.
- Frederick Friedel (grandmaster), Newsday, May 9, 1997

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

http://www-unix.mcs.anl.gov/~mccune/papers/robbins/

1999: Remote Agent takes Deep Space 1 on a galactic ride

For two days in May, 1999, an AI Program called Remote Agent

autonomouslyran Deep Space 1 (some 60,000,000 miles from earth)

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

Proverb 1999: Solving Crossword Puzzles as Probabilistic Constraint Satisfaction

Proverb solves

crossword puzzles

better than most humans

Michael Littman et a. 99

Robocup @ Cornell199

http://www.mae.cornell.edu/raff/MultiAgentSystems/MultiAgentSystems.htm

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.

DARPA - Urban Challenge (2007)

- The Urban Challenge features autonomous ground vehicles maneuvering in a mock city environment, executing simulated military supply missions while merging into moving traffic, navigating traffic circles, negotiating busy intersections, and avoiding obstacles.

Many Other Applications

- Financial planning
- Marketing
- E-business
- Telecommunications
- Manufacturing
- Operations Management
- Production Planning
- Transportation Planning
- System Design
- Health Care

Goals of INFO 372

Focus of Info 372: Problem Solving

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;

Goals of INFO 372 Students learn about the tradeoffs in modeling choices.;

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

- Concrete examples to move from one representation modeling formalism to another formalism;

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