Carla P. Gomes INFO372 CS-INFO 372: Explorations in Artificial Intelligence Prof. Carla P. Gomes...
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Transcript of 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
Carla P. GomesINFO372
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
Carla P. GomesINFO372
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.
Carla P. GomesINFO372
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
Carla P. GomesINFO372
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
Carla P. GomesINFO372
Overview of this Lecture
• Course Administration
• What is Artificial Intelligence?
• Course Themes, Goals, and Syllabus
Carla P. GomesINFO372
What is Intelligence?Historical Perspective of AI
State-of-the-art and Challenges
What is Artificial Intelligence (AI)?
Carla P. GomesINFO372
What is AI?
Ambitious goals:– understand “intelligent” behavior
– build “intelligent” agents
Carla P. GomesINFO372
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
Carla P. GomesINFO372
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…
Carla P. GomesINFO372
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.
Carla P. GomesINFO372
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.
Carla P. GomesINFO372
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
Carla P. GomesINFO372
Some Famous Imitation Games
• 1960s ELIZA Joseph Weizenbaum
– Rogerian psychotherapist
• 1990s ALICE
• Loebner prize– win $100,000 if you pass the test
Carla P. GomesINFO372
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
Carla P. GomesINFO372
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.
Carla P. GomesINFO372
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:
Carla P. GomesINFO372
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).
Carla P. GomesINFO372
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)
Carla P. GomesINFO372
• 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.
Carla P. GomesINFO372
What is AI?
Thinking humanly
Thinking
Rationally
Acting
Humanly
Acting
Rationally
Thought/Reasoning
Behavior/Actions
Human-likeIntelligence
“Ideal” Intelligent/Rationally
Carla P. GomesINFO372
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
Carla P. GomesINFO372
1997:Deep Blue beats the World Chess Champion
I could feel human-level intelligence across the room -Gary Kasparov, World Chess Champion (human…)
vs.
Carla P. GomesINFO372
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!
Carla P. GomesINFO372
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
Carla P. GomesINFO372
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
Carla P. GomesINFO372
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.”
Carla P. GomesINFO372
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.
Carla P. GomesINFO372
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
Carla P. GomesINFO372
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.
Carla P. GomesINFO372
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!
Carla P. GomesINFO372
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 !
Carla P. GomesINFO372
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
Carla P. GomesINFO372
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.
Carla P. GomesINFO372
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
Carla P. GomesINFO372
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
Carla P. GomesINFO372
Proverb 1999: Solving Crossword Puzzles as Probabilistic Constraint Satisfaction
Proverb solves crossword puzzles better than most humans
Michael Littman et a. 99
Carla P. GomesINFO372
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.
Carla P. GomesINFO372
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.
Carla P. GomesINFO372
Many Other Applications
• Financial planning• Marketing• E-business• Telecommunications• Manufacturing• Operations Management• Production Planning• Transportation Planning• System Design• Health Care
Carla P. GomesINFO372
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;
Carla P. GomesINFO372
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;