Artificial Intelligence

Spring 2008, Juris Vīksna

Introduction

Outline

What is AI? Subjects covered in the course Requirements Textbooks Other practical information

What is AI?

General definition:

AI is the branch of computer science that is concerned with the automation of intelligent behavior.

what is intelligent behavior? is intelligent behavior the same for a computer and a human?

What is AI?

at least we have experience with human intelligence

possible definition: intelligence is the ability to form plans to achieve goals by interacting with an information-rich environment

Tighter definition:

AI is the science of making machines do things that would require intelligence if done by people. (Minsky)

What is AI?

Intelligence encompasses abilities such as:

understanding language perception learning reasoning

Self-defeating definition:

AI is the science of automating intelligent behaviors currently achievable by humans only.

this is a common perception by the general public as each problem is solved, the mystery goes away and it's no longer

"AI"

successes go away, leaving only unsolved problems

What is AI?

AI ranges across many disciplines

computer science, engineering, cognitive science, logic, … research often defies classification, requires a broad context

Self-fulfilling definition:

AI is the collection of problems and methodologies studied by AI researchers.

What is AI?

Pre-history of AI

the quest for understanding & automating intelligence has deep roots 4th cent. B.C.: Aristotle studied mind & thought, defined formal logic

14th–16th cent.: Renaissance thought built on the idea that all natural or artificial processes could be mathematically analyzed and understood

18th cent.: Descartes emphasized the distinction between mind & brain (famous for "Cogito ergo sum")

19th cent.: advances is science & understanding nature made the idea of creating artificial life seem plausible

Shelley's Frankenstein raised moral and ethical questions Babbage's Analytical Engine proposed a general-purpose, programmable computing

machine -- metaphor for the brain

19th-20th cent.: saw many advances in logic formalisms, including Boole's algebra, Frege's predicate calculus, Tarski's theory of reference

20th cent.: advent of digital computers in late 1940's made AI a viable Turing wrote seminal paper on thinking machines (1950)

Pre-history of AI

birth of AI occurred when Marvin Minsky & John McCarthy organized the Dartmouth Conference in 1956 brought together researchers interested in "intelligent machines" for next 20 years, virtually all advances in AI were by attendees

Minsky (MIT), McCarthy (MIT/Stanford), Newell & Simon (Carnegie),…

John McCarthyMarvin Minsky

History of AI

the history of AI research is a continual cycle of optimism & hype reality check & backlash refocus & progress

…

1950's – birth of AI, optimism on many frontsgeneral purpose reasoning, machine translation, neural computing, …

first neural net simulator (Minsky): could learn to traverse a mazeGPS (Newell & Simon): general problem-solver/planner, means-

end analysisGeometry Theorem Prover (Gelertner): input diagrams, backward

reasoningSAINT(Slagle): symbolic integration, could pass MIT calculus

exam

History of AI

1960's – failed to meet claims of 50's, problems turned out to be hard!

so, backed up and focused on "micro-worlds"within limited domains, success in: reasoning, perception,

understanding, …

• ANALOGY (Evans & Minsky): could solve IQ test puzzle• STUDENT (Bobrow & Minsky): could solve algebraic word

problems• SHRDLU (Winograd): could manipulate blocks using robotic arm,

explain self• STRIPS (Nilsson & Fikes): problem-solver planner, controlled

robot "Shakey"• Minsky & Papert demonstrated the limitations of neural nets

History of AI

1970's – results from micro-worlds did not easily scale upso, backed up and focused on theoretical foundations,

learning/understanding

conceptual dependency theory (Schank)frames (Minsky)machine learning: ID3 (Quinlan), AM (Lenat)

practical success: expert systems

DENDRAL (Feigenbaum): identified molecular structureMYCIN (Shortliffe & Buchanan): diagnosed infectious blood

diseases

History of AI

1980's – BOOM TOWN!

cheaper computing made AI software feasiblesuccess with expert systems, neural nets revisited, 5th Generation

Project

• XCON (McDermott): saved DEC ~ $40M per year• neural computing: back-propagation (Werbos), associative

memory (Hopfield)• logic programming, specialized AI technology seen as future

History of AI

1990's – again, failed to meet high expectations

so, backed up and focused : embedded intelligent systems, agents, …hybrid approaches: logic + neural nets + genetic algorithms + fuzzy +

…

• CYC (Lenat): far-reaching project to capture common-sense reasoning

• Society of Mind (Minsky): intelligence is product of complex interactions of simple agents

• Deep Blue (formerly Deep Thought): defeated Kasparov in Speed Chess in 1997

History of AI

Development of AI

General Problem Solvers (1950’s) Power (1960’s) “Romantic” Period (mid 1960’s to mid 1970’s) Knowledge-based Approaches (mid 1970’s to mid

1990’s) Biological and Social Models (mid 1990’s to current)

General problem solvers

use a generalized problem solving method (divide up problems, work forward, work backward) and apply approach to a VERY BROAD range of problems.

limitations: hardware capabilities sometimes called "weak solution methods"

LOGIC THEORIST could prove 38 of the first 52 theorems in the Principia

Mathematica Journal of Symbolic Logic declined publishing an article with Logic

Theorist as a co-author

GENERAL PROBLEM SOLVER used means-ends analysis to reduce the difference between the

current state and desired (end) state handled mathematical logic problems, hence not as general as

originally hoped

Examples of General Problem Solvers

Power

Throw immense computing power at certain types of problems

example: CHESS examine 10120 moves to consider all possibilities.

advances that made this approach possible: more powerful hardware languages developed specifically for AI applications

Examples of General Problem Solvers

Romantic period

Focus on computer understanding Hence concentrated on:

natural language - particularly stories and dialog.

Fundamental problem: what is "understanding"? Can a computer be said to understand things? Not clear how humans understand things, for instance

reading in young children.

SHRDLU - developed by Winograd understood and reasoned about its block world

ELIZA - Rogerian therapist Newell and Simon examined human problem solving via

protocol analysis to determine how humans understand things. Then created computer programs to generate a similar trace.

Examples of Romantic Period

SHRLDU has just completed the command:“Find a block which is taller than the one you are holding and put on the box”Example of microworld.

SHRLDU

Eliza sample dialog

HUMAN: You are like my father in some ways. ELIZA: What resemblance do you see? HUMAN: You are not very aggressive, but I think you don’t want me

to notice that. ELIZA: What makes you think I am not very aggressive? HUMAN: You don’t argue with me. ELIZA: Why do you think I don’t argue with you? HUMAN: You are afraid of me. ELIZA: Does it please you to believe I am afraid of you? HUMAN: My father is afraid of everybody. ELIZA: What else comes to mind when you think of your father? HUMAN: Bullies.

Knowledge based approaches

Flaws of previous approaches considered: General problem solving tries to apply a single

solution approach to a wide range of problems. The general approaches were not as general as hoped and more problem specific approaches could be more powerful and simpler.

Knowledge based approaches

Power approach tried to program optimal (highest probability) approach. Human experts use HEURISTICS (rules of thumb) to find a solution.

Example: Chess masters don't look ahead very many moves, as a POWER approach implies. Instead they choose from a set of ‘good’ alternatives.

Knowledge based approaches

Romantic period: true understanding may not be necessary to achieve useful results.

Feigenbaum, in a speech at Carnegie, challenged his former professors to stop looking at "toy problems" and apply AI techniques to "real problems".

The key to solving real world problems is that these system handle only a very specific problem area, a "narrow domain".

Biological and Social Models

Neural Networks (connectionist models in the text book) Based on the brain’s ability to adapt to the world by modifying

the relationships between neurons.

Genetic algorithms attempt to replicate biological evolution. Populations of competing solutions are generated. Poor solutions die out, better ones survive and reproduce with

‘mutations’ created.

Software agents Semi-autonomous agents, with little knowledge of other agents

solve part of a problem, which is reported to other agents. Through the efforts of many agents a problem is solved.

Neural networks

Neural networks

Genetic algorithms

Genetic algorithms

Philosophical extremes in AI

Neats vs. Scruffies

Neats focus on smaller, simplified problems that can be well-understood, then attempt to generalize lessons learned

Scruffies tackle big, hard problems directly using less formal approaches

GOFAIs vs. Emergents

GOFAI (Good Old-Fashioned AI) works on the assumption that intelligence can and should be modeled at the symbolic level

Emergents believe intelligence emerges out of the complex interaction of simple, sub-symbolic processes

Philosophical extremes in AI

Weak AI vs. Strong AI

Weak AI believes that machine intelligence need only mimic the behavior of human intelligence

Strong AI demands that machine intelligence must mimic the internal processes of human intelligence, not just the external behavior

Different views of AI

Strong view The effort to develop computer-based systems that behave

as humans. Argues that an appropriately programmed computer really is

a mind, that understands and has cognitive states. “The study is to proceed on the basis of the conjecture that

every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate.” (From Dartmouth conference.)

Different views of AI

Weak view Use “intelligent” programs to test theories about how

human beings carry out cognitive operations. AI is the study of mental faculties through the use of

computational models. Computer-based system that acts in such a way (i.e.,

performs tasks) that if done by a human we would call it ‘intelligent’ or ‘requiring intelligence’.

Criteria for success

long term: Turing Test (for Weak AI) as proposed by Alan Turing (1950), if a computer can make people

think it is human (i.e., intelligent) via an unrestricted conversation, then it is intelligent

Turing predicted fully intelligent machines by 2000, not even close Loebner Prize competition, extremely controversial

short term: more modest success in limited domains performance equal or better than humans

e.g., game playing (Deep Blue), expert systems (MYCIN)

real-world practicality $$$e.g., expert systems (XCON, Prospector), fuzzy logic (cruise

control)

HAL’s last words, “2001: A Space Odyssey”

HAL’s last words, “2001: A Space Odyssey”

“Good afternoon, gentleman. I am HAL 9000 computer. I became operational at the HAL plant in Urbana, Ill., on the 12th of January, 1992. My instructor was Mr. Langley and he taught me to sing a song. If you’d like to hear it, I can sing it for you.”

Turing test

Experimenter

AI system

Control

Appeal of the Turing Test

Provides an objective notion of intelligence, i.e., compare intelligence of the system to something that is considered intelligent, avoiding debates over what is intelligence.

Avoids debates of whether or not the system uses correct internal processes.

Eliminates biases toward living organisms since experimenter communicates with both the AI system and the control (human) in the same manner.

Alan Turing

Weaknesses of the Turing Test

The breadth of the test is nearly impossible to achieve.

Some systems exhibit characteristics similar to Turing’s criteria, yet we would not label them ‘intelligent;’ e.g., ELIZA is easy to unmask, it cannot pass a true interrogation.

Focuses on symbolic, problem solving ignores perceptual skills and manual dexterity which are important components of human intelligence.

By focusing on replicating human intelligence, researchers may be distracted from the tasks of developing theories that explain the mechanisms of human and machine intelligence and applying the theories to solving actual problems.

The Chinese Room

Set of rules, in English, for

transforming phrases

Chinese Writing is given to

the person

Correct Responses

She does not know Chinese

The Chinese Room Scenario

An individual is locked in a room and given a batch of Chinese writing. The person locked in the room does not understand Chinese.

Next she is given more Chinese writing and a set of rules (in English which she understands) on how to collate the first set of Chinese characters with the second set of Chinese characters.

If the person becomes good at manipulating the Chinese symbols and the rules are good enough, then to someone outside the room it appears that the person understands Chinese.

Does the person understand Chinese?

Why? Why not?

The Chinese Room (cont.)

Searle's, who developed the argument, point is that she doesn't really understand Chinese, she really only follows a set of rules.

Following this argument, a computer could never be truly intelligent, it is only manipulates symbols. The computer does not understand the semantic context.

Searle’s criteria is “intentionality,” the entity must be intentionally exhibiting the behavior, not simply following a set of rules.

Intentionality is as difficult to define as intelligence. Searle excludes ‘weak AI’ from his argument against the possibility

of AI.

Searle’s argument created a huge response

This religious diatribe against AI, masquerading as a serious scientific argument, is one of the wrongest, most infuriating articles I have ever read in my life. ... I know that this journal is not the place for philosophical and religious commentary, yet it seems to me that what Searle and I have is, at the deepest level, a religious disagreement and I doubt that anything I say could ever change his mind. He insists on things he calls "causal intentional properties" which seem to vanish as soon as you analyze them, find rules for them, or simulate them. But what those things are, other than epiphenomena, or innocently emergent qualities I don't know.

Goedel’s Theorem

The halting problemFor a given computer program P and given input data x, output “yes” if the computation P(x) terminates and output “no” otherwise.

The halting problem is undecidable (i.e. it is not solvableby any computer program).

Goedel’s Theorem

S(x) =

1, Px(x) terminates

0, otherwise

T(x) =

Px(x) + 1, Px(x) terminates

0, otherwise

Goedel’s Theorem

T = Pk

Pk(k) =

Pk(k) + 1, Pk(k) terminates

0, otherwise

Goedel’s Theorem

M - an “intelligent” program

M(x) =

Px(x) + 1, Px(x) terminates

0 or does not terminate, otherwise

M = Pk

Goedel’s Theorem

M = Pk - an “intelligent” program

Pk(k) =

Pk(k) + 1, Pk(k) terminates0 or, does not terminate, otherwise

What is artificial intelligence?

Arguments about AI seem to rapidly break down into philosophical debates where there is probably no absolute right or wrong answer.

Note Hofstadter's comments about "religious" disagreement. It often comes down to considering the pros and cons of both sides, realizing that neither is completely right (or completely wrong) and taking a stand for one or the other.

Which side you tend to fall on will, almost unavoidably, be based on personal values.

No universally accepted definition of intelligence. Definitions of intelligence is subject to change, which

makes it difficult to aim for! Similar to the situation in linguistics and for comparative psychologists that have taught primates sign language.

"The Ultimate Limits of AI” - notice that these are really sociological questions.

This course will focus what has been achieved in AI. However, be aware of these issues.

Summary

Branches of AI

Games - study of state space search, e.g., chess Automated reasoning and theorem proving, e.g., logic

theorist Expert/Knowledge-based systems Natural language understanding and semantic modeling Model human cognitive performance Robotics and planning Automatic programming Learning Vision

Subjects covered in the course State space representations and search algorithms 3 Decomposition spaces 2 Game playing 2 Automated reasoning (resolution methods) 3 Neural networks 1 Expert systems (???) 1 Learning (Decision trees, Genetic algorithms, HMMs) 3

“Typical” AI subjects likely not to be covered

Natural language processing Knowledge representation Planning systems “AI programming languages” - LISP, PROLOG etc.

Requirements 2-4 theoretical homeworks

Must be submitted before the exam session

40% for all homeworks Programming assignment

Problem to be announced early in MarchNo deadline – must be submitted before the exam40%

Exam20%

Optional

To qualify for grade 10 you may be asked to cope with some additional question(s)/problem(s)

Academic honesty

You are expected to submit only your own work!

Sanctions:

Receiving a zero on the assignment (in no circumstances a resubmission will be allowed)

No admission to the exam and no grade for the course

Textbooks

Nils J. Nillson

Problem-Solving Methods in Artificial Intelligence

McGraw-Hill, 1971.

Textbooks

Nils J. Nillson

Principles of Artificial Intelligence

Morgan Kaufmann, 1986

Textbooks

Nils J. Nillson

Artificial Intelligence: a New Synthesis  

Morgan Kaufmann, 1998

Textbooks

Rajan Shinghal

Formal Concepts in Artificial Intelligence

Chapman & Hall, 1992

Textbooks

George F.LugerWilliam A.StubblefieldRonald L.Rivest

Artificial Intelligence and the design of Expert Systems

Benjamin/Cummings, 1989

Textbooks

Elaine Rich Kevin Knight

Artificial Intelligence

McGraw-Hill, 1991

Textbooks

Judea Pearl

Intelligent search strategies for computer problem solving

Addison-Wesley, 1984

Textbooks

Nirmal .K.BosePing Liang

Neural Network Fundamentalswith Graphs, Algorithms and Applications

McGraw-Hill, 1996

Textbooks

Roger Penrose

The emperors new mind

Web page

http://susurs.mii.lu.lv/juris/courses/ai2008.html

Is expected to contain:

• short summaries of lectures• announcements• power point presentations (when available)• homework and programming assignment problems• your grades (???)• other relevant information

Contact information

Juris Vīksna

Room 421, Rainis boulevard 29

phone: 67213716