carla p. gomes info372 cs-info 372: explorations in artificial intelligence prof. carla p. gomes...

Carla P. GomesINFO372

CS-INFO 372:Explorations in Artificial Intelligence

Prof. Carla P. [email protected]

Introduction

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


Lectures: Tuesday and Thursday - 10:10 - 11:25Location: Phillips Hall, room 307

Lecturer: Prof. GomesOffice: 5133 Upson HallPhone: 255 9189Email: [email protected]

Administrative Assistant: Beth Howard ([email protected]) 5136 Upson Hall, 255-4188

TAs: Robert Xiao [email protected] Yunsong Guo <[email protected]>

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

INFO372 – Explorations inArtificial IntelligenceCourse Administration


Office Hours

TAs:

Robert Xiao [email protected] TBAYunsong 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

Linear Programming by Vasek Chvatal

Principles of Constraint Programming

By Krzysztof Apt


Overview of this Lecture

• Course Administration

• What is Artificial Intelligence?

• Course Themes, Goals, and Syllabus


What is Intelligence?Historical Perspective of AI

State-of-the-art and Challenges

What is Artificial Intelligence (AI)?


What is AI?

Ambitious goals:– understand “intelligent” behavior

– build “intelligent” agents


What is Intelligence?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 sciencecomputer science and engineeringmathematics and physics


AI Leverages from Different Disciplines

AI Leverages from Different Disciplines

Philosophye.g., foundational issues in logic, methods of reasoning, mind as physical system, foundations of learning, language, rationality

Computer science and engineeringe.g., complexity theory, algorithms, logic and inference, programming languages, and system building (hardware and software).

Mathematics and physicse.g., statistical modeling, continuous mathematics, Markovmodels, statistical physics, and complex systems.

and others, e.g., cognitive science, neuroscience, economics, psychology, linguistics, statistics…


AI:Historical Perspective

AI:Historical Perspective

Obtaining an understanding of the human mind is one of thefinal 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)

Alan Turing

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.

http://www.computer50.org/kgill/people/turpic.gif


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

• 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 passesif interrogator cannot tell which one is the machine

Alan Turing


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

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.

1960s ELIZA Joseph Weizenbaum


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.

http://www.loebner.net/Prizef/loebner-prize.html


Acting humanly: Turing Test

• 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?

Turing test identified key research areas in AI:


Different ApproachesDifferent 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).


Man vs. Machiens The HardwareMan 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)


Computer vs. Brain


• 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.


What is AI?

Thinking humanly

Thinking

Rationally

Acting

Humanly

Acting

Rationally

Thought/Reasoning

Behavior/Actions

Human-likeIntelligence

“Ideal” Intelligent/Rationally


What's involved in Intelligence?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 Planningmodelling 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


A few examples…

State-of-the-art Reasoning and Planning in AI


1997:Deep Blue beats the World Chess Champion

I could feel human-level intelligence across the room -Gary Kasparov, World Chess Champion (human…)

vs.


Deep Blue vs. Kasparov

One of the most famous modern computers, Deep Blue, which defeated Gary Kasparov at chess.

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” ( acrossthe board from him)Kasparov 1997

The value of IBM’s stockIncreased by $18 Billion!


How Intelligent is Deep Blue?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 2On 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 BlueKasparov 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 SearchGame 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.


Game Tree Search


History of Search InnovationsHistory 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 TablesTransposition 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 HardwareSpecial-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 BlueDeep Blue

• Hardware– 32 general processors

– 220 VSLI chess chips

• Overall: 200,000,000 positions per second– 5 minutes = depth 14

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


Tactics into StrategyTactics 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


1996 - EQP:

Robbin’s Algebras are all boolean

[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/

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.


For two days in May, 1999, an AI Program called Remote Agent autonomously ran Deep Space 1 (some 60,000,000 miles from earth)

Real-time ExecutionAdaptive Control

Hardware

Scripted

Executive

GenerativePlanner &Scheduler

Generative Mode Identification

& Recovery

Scripts

Mission-levelactions &resources

component models

ESL

Monitors

GoalsGoals

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


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, 2005Stanley and the Stanford RacingTeamwere 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


Course Themes, Goals, and Syllabus


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

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;

carla p. gomes info372 cs-info 372: explorations in artificial intelligence prof. carla p. gomes...

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