cmsc 671 fall 2005 professor marie desjardins, [email protected], ite 337, x53967...
TRANSCRIPT
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Today’s class
• Course overview
• Introduction– Brief history of AI
– What is AI? (and why is it so cool?)
– What’s the state of AI now?
• Lisp – a first look
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Course OverviewCourse Overview
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Course materials
• Course website: http://www.cs.umbc.edu/courses/graduate/671/fall05/– Course description and policies (main page)– Course syllabus, schedule (subject to change!), and slides– Pointers to homeworks and papers (send me URLs for interesting /
relevant websites, and I’ll add them to the page!)
• Course mailing list: [email protected]– Send mail to [email protected]– subscribe cs671 Your Name– Send general questions to the list– Requests for extensions, inquiries about status, requests for
appointments should go directly to Prof. desJardins
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Homework and grading policies
• Six homework assignments (mix of written and programming)• Due every other Thursday (approximately) at the beginning of class• One-time extensions of up to a week will generally be granted if
requested in advance• Last-minute requests for extensions will be denied• Late policy:
– .000001 to 24 hours late: 25% penalty– 24 to 48 hours late: 50% penalty– 48 to 72 hours late: 75% penalty– More than 72 hours late: no credit will be given
• NOTE ON READING: Please do the reading before each class!
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Academic integrity• Instructor’s responsibilities:
– Be respectful– Be fair– Be available– Tell the students what they need to know and how they will be
graded
• Students’ responsibilities:– Be respectful– Do not cheat, plagiarize, or lie, or help anyone else to do so– Do not interfere with other students’ academic activities
• Consequences include (but are not limited to) a reduced or failing grade on the assignment, or in the class
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Instructor availability
• Prof. desJardins– Official office hours: Tues. 1:00-2:00, Wed. 11:30-12:30
(ITE 337)
– Appointments may also be made by request (24 hours notice is best)
– Drop in whenever my door is open (see posted “semi-open door policy”)
– Will try to respond to e-mail within 24 hours
– Direct general questions (i.e., those that other students may also be wondering about) to the class mailing list
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What is AI??What is AI??
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History
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Eliza• ELIZA: A program that simulated a psychotherapist
interacting with a patient and successfully passed the Turing Test.
• Coded at MIT during 1964-1966 by Joel Weizenbaum.
• First script was DOCTOR. – The script was a simple collection of syntactic patterns not unlike
regular expressions
– Each pattern had an associated reply which might include bits of the input (after simple transformations (my your)
• Weizenbaum was shocked at reactions: – Psychiatrists thought it had potential.
– People unequivocally anthropomorphized.
– Many thought it solved the NL problem.
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Eliza• I am the psychotherapist. Please, describe your problems. Each
time you are finished talking, type RET twice.
• i'm having a hard time with my students
• Why do you say that?
• well, they keep asking me for money
• Maybe your plans have something to do with this.
• what plans?
• Is it because of your life that you say what plans?
• No, it's because I didn't know what you were talking about
• Is it because you didn't know what I was talking about that you came to me?
• no, it's because i wanted to see if you could pass the turing test
• Does it bother you that you wanted to see if I could really pass the turing test?
• …..
• How do you reconcile problems at school?
• This is a waste of time. Good bye
• My secretary will send you a bill.
• I am the psychotherapist. Please, describe your problems. Each time you are finished talking, type RET twice.
• i'm having a hard time with my students
• Why do you say that?
• well, they keep asking me for money
• Maybe your plans have something to do with this.
• what plans?
• Is it because of your life that you say what plans?
• No, it's because I didn't know what you were talking about
• Is it because you didn't know what I was talking about that you came to me?
• no, it's because i wanted to see if you could pass the turing test
• Does it bother you that you wanted to see if I could really pass the turing test?
• …..
• How do you reconcile problems at school?
• This is a waste of time. Good bye
• My secretary will send you a bill.
• This is a transcript of a conversation with an Eliza-like program found in Emacs
• Try typing
Meta-x doctor
in your favorite Emacs.
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Colby’s PARRY
• Kenneth Colby modeled a paranoid using the same techniques circa 1968.
• PARRY has basic emotions. If it gets angry, its replies become more hostile.
• In a Turing test, expert psychiatrists were unable to distinguish PARRY’s ramblings from those of real paranoids.
H: Good afternoon. My name is Doctor Jones.
P: Pleased to meet you. H: How are you? P: All right I guess. H: Could you tell me your name? P: Pat Smith. H: I though you might have some
problems. P: I've been upset lately. H: By what? P: People get on my nerves
sometimes. H: Tell me more. P: Do you know anything about
bookies? ...
H: Good afternoon. My name is Doctor Jones.
P: Pleased to meet you. H: How are you? P: All right I guess. H: Could you tell me your name? P: Pat Smith. H: I though you might have some
problems. P: I've been upset lately. H: By what? P: People get on my nerves
sometimes. H: Tell me more. P: Do you know anything about
bookies? ...
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Parry meets Eliza
P: I don't understand your motives.
E: Why don't you understand my motives?
P: Shouldn't I be?
E: Why do you say that just now?
P: That's just the way it is.
E: Suppose that's the way it were not.
P: So?
E: I am not sure I understand you fully.
P: You harp on the same questions too much.
E: What does that similarity suggest to you?
P: I don't get you.
E: Do you wish that you did get I?
P: I don't understand your motives.
E: Why don't you understand my motives?
P: Shouldn't I be?
E: Why do you say that just now?
P: That's just the way it is.
E: Suppose that's the way it were not.
P: So?
E: I am not sure I understand you fully.
P: You harp on the same questions too much.
E: What does that similarity suggest to you?
P: I don't get you.
E: Do you wish that you did get I?
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Foundations of AIComputerScience &
Engineering
AI
Mathematics
CognitiveScience
Philosophy
Psychology Linguistics
BiologyEconomics
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Big questions
• Can machines think?
• If so, how?
• If not, why not?
• What does this say about human beings?
• What does this say about the mind?
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Why AI?
• Engineering: To get machines to do a wider variety of useful things– e.g., understand spoken natural language, recognize individual people
in visual scenes, find the best travel plan for your vacation, etc.
• Cognitive Science: As a way to understand how natural minds and mental phenomena work– e.g., visual perception, memory, learning, language, etc.
• Philosophy: As a way to explore some basic and interesting (and important) philosophical questions– e.g., the mind body problem, what is consciousness, etc.
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What’s easy and what’s hard?• It’s been easier to mechanize many of the high-level tasks
we usually associate with “intelligence” in people
– e.g., symbolic integration, proving theorems, playing chess, medical diagnosis
• It’s been very hard to mechanize tasks that lots of animals can do– walking around without running into things
– catching prey and avoiding predators
– interpreting complex sensory information (e.g., visual, aural, …)
– modeling the internal states of other animals from their behavior
– working as a team (e.g., with pack animals)
• Is there a fundamental difference between the two categories?
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Turing Test
• Three rooms contain a person, a computer, and an interrogator.
• The interrogator can communicate with the other two by teleprinter.
• The interrogator tries to determine which is the person and which is the machine.
• The machine tries to fool the interrogator into believing that it is the person.
• If the machine succeeds, then we conclude that the machine can think.
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The Loebner contest • A modern version of the Turing Test, held annually, with a
$100,000 cash prize.• Hugh Loebner was once director of UMBC’s Academic
Computing Services (née UCS)• http://www.loebner.net/Prizef/loebner-prize.html• Restricted topic (removed in 1995) and limited time. • Participants include a set of humans and a set of computers
and a set of judges.• Scoring
– Rank from least human to most human. – Highest median rank wins $2000. – If better than a human, win $100,000. (Nobody yet…)
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What can AI systems do?Here are some example applications• Computer vision: face recognition from a large set• Robotics: autonomous (mostly) automobile• Natural language processing: simple machine translation• Expert systems: medical diagnosis in a narrow domain• Spoken language systems: ~1000 word continuous speech• Planning and scheduling: Hubble Telescope experiments• Learning: text categorization into ~1000 topics• User modeling: Bayesian reasoning in Windows help (the
infamous paper clip…)• Games: Grand Master level in chess (world champion),
checkers, etc.
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What can’t AI systems do yet?• Understand natural language robustly (e.g., read and
understand articles in a newspaper)
• Surf the web
• Interpret an arbitrary visual scene
• Learn a natural language
• Play Go well
• Construct plans in dynamic real-time domains
• Refocus attention in complex environments
• Perform life-long learning
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Who does AI?
• Academic researchers (perhaps the most Ph.D.-generating area of computer science in recent years)– Some of the top AI schools: CMU, Stanford, Berkeley, MIT,
UIUC, UMd, U Alberta, UT Austin, ... (and, of course, UMBC!)
• Government and private research labs– NASA, NRL, NIST, IBM, AT&T, SRI, ISI, MERL, ...
• Lots of companies!– Google, Microsoft, Honeywell, Teknowledge, SAIC, MITRE,
Fujitsu, Global InfoTek, BodyMedia, ...
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What do AI people (and the applications they build) do?
• Represent knowledge
• Reason about knowledge
• Behave intelligently in complex environments
• Develop interesting and useful applications
• Interact with people, agents, and the environment
• IJCAI-03 subject areas
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Representation
• Causality
• Constraints
• Description Logics
• Knowledge Representation
• Ontologies and Foundations
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Reasoning
• Automated Reasoning
• Belief Revision and Update
• Diagnosis
• Nonmonotonic Reasoning
• Probabilistic Inference
• Qualitative Reasoning
• Reasoning about Actions and Change
• Resource-Bounded Reasoning
• Satisfiability
• Spatial Reasoning
• Temporal Reasoning
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Behavior
• Case-Based Reasoning
• Cognitive Modeling
• Decision Theory
• Learning
• Planning
• Probabilistic Planning
• Scheduling
• Search
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Evolutionary optimization
• MERL: evolving ‘bots
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Interaction
• Cognitive Robotics
• Multiagent Systems
• Natural Language
• Perception
• Robotics
• User Modeling
• Vision
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Robotics• SRI: Shakey / planning ..\movies\sri-Shakey.ram
• SRI: Flakey / planning & control ..\movies\sri-Flakey.ram
• UMass: Thing / learning & control ..\movies\umass_thing_irreg.mpeg..\movies\umass_thing_quest.mpeg..\movies\umass-can-roll.mpeg
• MIT: Cog / reactive behavior ..\movies\mit-cog-saw-30.mov ..\movies\mit-cog-drum-close-15.mov
• MIT: Kismet / affect & interaction ..\movies\mit-kismet.mov..\movies\mit-kismet-expressions-dl.mov
• CMU: RoboCup Soccer / teamwork & coordination..\movies\cmu_vs_gatech.mpeg
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Applications• AI and Data Integration• AI and the Internet• Art and Creativity• Information Extraction
• A sample from IAAI-03:– Scheduling train crews– Automated student essay evaluation– Packet scheduling in network routers– Broadcast news understanding– Vehicle diagnosis– Robot photography– Relational pattern matching
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AI & art: NEvAr
• See http://eden.dei.uc.pt/~machado/NEvAr
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Protein folding
• MERL: constraint-based approach
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Interaction: Sketching
• MIT sketch tablet
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Other topics/paradigms
• Intelligent tutoring systems
• Agent architectures
• Mixed-initiative systems
• Embedded systems / mobile autonomous agents
• Machine translation
• Statistical natural language processing
• Object-oriented software engineering / software reuse
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LISPLISP
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Why Lisp?
• Because it’s the most widely used AI programming language
• Because Prof. desJardins likes using it
• Because it’s good for writing production software (Graham article)
• Because it’s got lots of features other languages don’t
• Because you can write new programs and extend old programs really, really quickly in Lisp
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Why all those parentheses?
• Surprisingly readable if you indent properly (use built-in Lisp editor in emacs!)
• Makes prefix notation manageable
• An expression is an expression is an expression, whether it’s inside another one or not
• (+ 1 2)• (* (+ 1 2) 3)• (list (* 3 5) ‘atom ‘(list inside a list) (list 3 4) ‘(((very) (very) (very) (nested list))))
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Basic Lisp types• Numbers (integers, floating-point, complex)
• Characters, strings (arrays of chars)
• Symbols, which have property lists
• Lists (linked cells)– Empty list: nil– cons structure has car (first) and cdr (rest)
• Arrays (with zero or more dimensions)
• Hash tables
• Streams (for reading and writing)
• Structures
• Functions, including lambda functions
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Basic Lisp functions
• Numeric functions: + - * / incf decf• List access: car (first), second … tenth, nth, cdr
(rest), last, length
• List construction: cons, append, list• Advanced list processing: assoc, mapcar, mapcan• Predicates: listp, numberp, stringp, atom, null, equal, eql, and, or, not
• Special forms: setq/setf, quote, defun, if, cond, case, progn, loop
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Useful help facilities
• (apropos ‘str) list of symbols whose name contains ‘str
• (describe ‘symbol) description of symbol• (describe #’fn) description of function• (trace fn) print a trace of fn as it runs• (print “string”) print output• (format …) formatted output (see Norvig p. 84)• :a abort one level out of debugger
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Great! How can I get started?• On sunserver (CS) and gl machines, run /usr/local/bin/clisp
• From http://clisp.cons.org you can download CLISP for your own PC (Windows or Linux)
• Great Lisp resource page: http://www.apl.jhu.edu/~hall/lisp.html
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