Artificial Intelligence

CS 165A

Tuesday, December 4, 2007

Finish BN Minds and Machines (Ch. 26)

Kurt Gödel (1906-1978)

• As a young man, he was part of the Vienna Circle – a group of philosophers, mathematicians and scientists in the 1920s who founded logical positivism– An important goal of philosophy is to develop and

study symbolic systems of logic, encompassing mathematics and empirical science

• Bertrand Russell showed that all of mathematics can be encapsulated in a formal logic system

• David Hilbert had previously shown that geometry was consistent if the arithmetic of real numbers was consistent. This guy set out to prove that arithmetic is consistent.

Austrian

• Gödel did not prove this – in fact, he proved that it was impossible!

• This led to his famous Incompleteness Theorem

Gödel

• Gödel’s Incompleteness Theorem– There are true propositions expressible in the system that are not

provable

– In other words: Truth Proof

– Even powerful logical systems cannot hope to encompass the full scope of mathematical truth

– Thus Gödel demonstrated that in any consistent formal system of mathematics sufficiently strong to allow one to do basic arithmetic, one can construct a statement about natural numbers that can be neither proven nor disproven within that system.

Alan Turing (1912-1954)

• Leibniz, Boole, Frege– Calculation as reasoning; foundations of logic

• Cantor, Hilbert, Gödel– Foundations of mathematics and logic

• John von Neumann– The renaissance man of computing

• Babbage, Ada, Burroughs, Hollerith, Bush, Zuse, Aiken, Atanasoff, Eckert, Mauchly, Wilkes, Hopper, Shannon…– Computing pioneers

British

• This man was at the intersection of the “thinkers” and the “builders”

Question from last class

If X and Y are independent, are they therefore independent given any variable(s)?

I.e., if P(X, Y) = P(X) P(Y) [ i.e., if P(X|Y) = P(X) ], can we conclude that P(X | Y, Z) = P(X | Z)?

The answer is no, and here’s a counter example:

X

Z

YWeight of Person A

Weight of Person B

Their combined weight

P(X | Y) = P(X)

P(X | Y, Z) ≠ P(X | Z)

5Note: Even though Z is a deterministic function of X and Y, it is still a random variable with a probability distribution

Constructing a BN

• Constructing a belief network for a given problem is somewhat of an art – there are several choices left to the designer– Variables (nodes)

– Influences (arcs)

– CPTs

• What is the effect of choosing “wrong” arcs?– Inaccurate conditional independences

Therefore wrong answers

– Difficult to construct CPTs

– May be computationally expensive

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Example

• Consider the relationship between Alzheimer’s (A) and loss of memory (M)

• We would generally want to know P(A | L)– What useful information is typically known?

• Which influence diagram makes more sense?

A

L

Alzheimer’s

Loss of memory

L

A Alzheimer’s

Loss of memory

…or…

Both are equally valid, since P(A, L) = P(A) P(L|A) = P(L) P(A|L)

P(L | A) and P(A)

Caveat

• It’s not always so obvious, however....

• Think about the relationship between:– Years of education and Occupation

– Cell phone usage and cancer

Inference in belief nets

• We’ve seen how to compute any probability from the belief net– This is probabilistic inference

P(Query | Evidence)– Since we know the joint probability, we can calculate anything via

marginalization P({red} | {green})

• However, things are usually not as simple as this– Structure is large or very complicated– Not all CPTs are known– Calculation by marginalization is often infeasible– Bayesian inference is NP hard!!

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Inference in belief nets (cont.)

• So in all but the most simple BNs, probabilistic inference is not really done just by marginalization

• Instead, there are practical algorithms for doing probabilistic inference– Remember: AI is “solving exponential problems in polynomial

time”

• For example, stochastic approximation techniques– Including Monte Carlo techniques

• We won’t cover these probabilistic inference algorithms though….

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Practical uses of Belief Nets

• Uses of belief nets:– Calculate probabilities of causes, given symptoms

– Calculating probabilities of symptoms, given multiple causes

– Calculating probabilities to be combined with an agent’s utility function

– Explaining the results of probabilistic inference to the user

– Deciding which additional evidence variables should be observed in order to gain useful information

Is it worth the extra cost to observe additional evidence?

– P(X | Y) vs. P(X | Y, Z)

– Performing sensitivity analysis to understand which aspects of the model affect the queries the most

How accurate must various evidence variables be? How will input errors affect the reasoning?

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Belief Networks

• There are lots of success stories for Bayesian networks– Typically for diagnostic inference

Business, science, medicine, HCI, databases…

– Some systems outperform the experts

• Unlike logic-based systems, getting it exactly right isn’t always critical

• Given good tools, domain experts (not just computer scientists!) can create good links and fill in reasonable probabilities– There are several BN software systems available

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Minds and Machines (Ch. 25)

Philosophical foundations of AI

The big questions

• How can minds work?– What is mind? Consciousness?– What is intelligence?– Is there is mind/body duality, or is it all “just” physical?– Is intelligence algorithmic?

• How do minds work?– What are the mechanisms that underpin human thought and

intelligence?– How is the brain organized? Basic neurophysiological principles?

• Can non-biological systems have minds?– Is “Strong AI” possible?– Are there limits to what computers can do?– What about moral choice, love, creativity…?– Will computers keep us around once they surpass us?

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The big questions

• These questions are not new– Plato, Aristotle, Descartes, Bacon, …Turing…

– Minsky, Simon, Newell, McCarthy…

– Searle, Dreyfus, Penrose…

• Has the attempt to produce general intelligence failed?

• What is fundamental to intelligence?– Logic and reasoning

Humans, chess

– Perception and action Cockroaches, survival

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In favor of strong AI

• Brains cause minds– Physicalism, materialism

– Atoms, molecules, neurotransmitters…

• Functionalism– The function of each “module” is what’s important, not how the

module is implemented (not the physical properties of the neurons)

• “Brain in a vat” thought experiments– Interesting moral/ethical implications

• Alan Turing seminal 1945 paper, “As We May Think”– Behavioral test for intelligence (the Imitation Game)

– Is this a good test for intelligence?

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Turing’s seminal AI paper

• Considers the question, “Can Machines Think?”– Too subjective, meaningless – rather, replace this question with an

operational definition of thinking/intelligence

• The Imitation Game

Computing Machinery and Intelligence (1950)

C

A

BInterrogator

Man

Woman

Human

Computer

Turing paper (cont.)

• The Turing Test– “Are there imaginable digital computers which would do well in

the imitation game?”

– I.e., Can a computer fool an interrogator into thinking it is a person?

• Properties of the Turing Test– Operational/functional/behavioral definition of intelligence

– Distinguishes between physical and intellectual capacities

– Question and answer method – language comprehension and generation

• Might there be other kinds of Turing Tests?– Emotional, physical, visual…

Digital Computers

• In 1950, computers were not household items!– Turing had to define digital computers

Distinguishes from “human computers”

– States basic Theory of Computation results regarding universality All digital computers are essentially equivalent Don’t need different machines for different tasks

– Main technical issues Adequate storage (109), Speed, Programming

• The key will be “learning machines”– Probabilistic (not completely determined)

– Simulate a child’s mind, then educate

Main Objections (Turing)

1. The Theological Objection– Thinking is part of the soul, which is particular to man

2. The 'Heads in the Sand' Objection  – I don’t want it to be true

3. The Mathematical Objection  – Godel’s Incompleteness Theorem

4. The Argument from Consciousness– How would we really know?

5. Arguments from Various Disabilities– Computers will never be able to do X

Main Objections (Turing)

6. Lady Lovelace's Objection– Computers can only do what we instruct them to do

7. Argument from Continuity in the Nervous System – The nervous system is analog

8. The Argument from Informality of Behaviour  – Rules cannot capture behavior

9. The Argument from Extra-Sensory Perception   – What if ESP is real…?

Insight from 1950

“We may hope that machines will eventually compete with men in all purely intellectual fields. But which are the best ones to start with?”

- Chess

- Understanding and speaking language

“I believe that in about fifty years time it will be possible to program computers with a storage capacity of about 109 to make them play the imitation game so well that an average interrogator will not have more than 70 per cent chance of making the right identification after five minutes of questioning.”

“I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted.”

Insight from 1950 (cont.)

“Instead of trying to produce a program to simulate the adult mind, why not rather try to produce one which simulates the child's? If this were then subjected to an appropriate course of education one would obtain the adult brain…. Our hope is that there is so little mechanism in the child-brain that something like it can be easily programmed.”

The Loebner Prize

The Chinese Room (Searle, 1980)

Let’s see…

Searle: There is no conscious understanding of Chinese in this system

• Neither the human nor the paper understands Chinese

Since this is analogous to a computer running an AI program, it must also be the case that a computer cannot be said to have conscious understanding

• Unconscious parts cannot make a conscious whole

(“…To get to the other side.”)

(“That’s funny!”)

小逸李建

建楼小効

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Simulating intelligence?

• Is a simulation of intelligence the same thing as intelligence?– Artificial light vs. artificial flowers

• Does a computer simulation of mental processes actually have mental processes?– Is a simulation of multiplication really multiplication?

Does it give the right answer?

– Is a simulation of a storm a real storm? Will it make us wet?

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The bottom line

• The jury is still out…– Endless (often repetitive) debate

• Beware of these discussions!– Be precise in your definitions of concepts– Intuition can be misleading– Analogy is not proof

• Distinguish between the technical feasibility and conceptual possibility of strong AI

• Meanwhile, perhaps focus on “weak” AI – building systems that useful and powerful and (dare we say) intelligent– AI still has a very bright and promising future

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Robotics

Chapter 25

Robotics

• Robots are physical agents that perform tasks by manipulating the physical world– Many other definitions, too…

• The physical version of the “sense-reason-act” agents that we’ve considered to define AI– Sensing/perception is very difficult

Not just adding a sentence to the KB

– High-level reasoning is sometimes minimal Lots of “low-level reasoning” though

– Action affects the physical world

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Applications

• Robots are used for– Exploration (outer space, ocean depths)

– Industry (welding, painting, inspecting, material handling, etc.)

– Medicine (telesurgery, prosthetics)

– Military (autonomous weapons, carriers, aircraft, subs), law enforcement (bomb disposal)

– Hazardous environments (nuclear cleanup, mines, space, planetary)

– Entertainment, toys (pets, Sony AIBO)

– Service (vacuum cleaners, humanoid robots, Honda ASIMO)

– …

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Brief history of robotics

• The word “robot” comes from the 1921 play “R.U.R.” (Rossum's Universal Robots) by the Czech writer Karel Capek.  “Robot” comes from the Czech word robota, meaning “forced labor.”

• 1941: Isaac Asimov first uses the term robotics to describe the technology of robots. He predicted the rise of the robot industry.

• Isaac Asimov’s Three Laws of Robotics: A robot may not injure humanity, or, though

inaction, allow humanity to come to harm. 1. A robot may not injure a human being, or, through

inaction, allow a human being to come to harm, unless this would violate a higher order law.

2. A robot must obey orders given it by human beings, except where such orders would confict with a higher order law.

3. A robot must protect its own existence as long as such protection does not conflict with a higher order law

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• 1960s: First commercial robot arm, the Unimate (from Unimation)

• Late 1970s: Development of the PUMA (Programmable Universal Machine for Assembly) arm; it is still in use

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• 1968: The General Electric Walking Truck was the first manual controlled walking truck.

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• 1968: SRI International built the first mobile robot with vision capability. “Shakey,” equipped with a television camera, a range finder and sensors, was the first mobile robot that could think and respond to the world around it.

• Mid-1980s: DARPA’s Autonomous Land Vehicle (ALV) program

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• Early 1980s: Hopping robots

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• 1990s: Robots in space exploration

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• 1990s: Robots in hazardous environments

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• 2000s: Robots in medicine

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• 2000s: Robot servants and pets

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DARPA Grand Challenge

“DARPA intends to conduct a challenge of autonomous ground vehicles between Los Angeles and Las Vegas in March of 2004. A cash award of $1 million will be granted to the team that fields the first vehicle to complete the designated route within a specified time limit.”

http://www.darpa.mil/grandchallenge

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Robot categories

• Robot manipulator– Physically anchored robot arm w/joints

• Mobile robot– Move using wheels, tracks, legs, etc.

– On land, in air, under water, on water…

• Hybrid mobile + manipulation– Manipulator connected to a mobile

platform

– Humanoid robot

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Robot components (hardware and software)

• Mechanical and electrical components– Chassis, motors, materials, power, …

• Sensors – Enable a robot to perceive the environment– Cameras, contact sensor, ultrasound, sonar, …

• Effectors – Enable a robot to assert physical forces on the environment– Robot arm, wheels, legs, head, …

• Planning – Software that decides what to do, where to go– Move to a location, avoid obstacles, grasp the object, …

• Control – Initiates and monitors robot movement– Motor forces and torques for speed, direction, path control, …

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