Artificial Intelligence
CS 165A
Tuesday, December 4, 2007
 Finish BN
 Minds and Machines (Ch. 26)
Kurt Gödel (1906-1978)
Austrian
• 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.
• 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)
British
• 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
• 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:
Weight of
Person A
X
Y
Z
Weight of
Person B
Their combined weight
P(X | Y) = P(X)
P(X | Y, Z) ≠ P(X | Z)
Note: Even though Z is a deterministic function of X and Y, it is still a random variable with a
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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? P(L | A) and P(A)
• Which influence diagram makes more sense?
A
Alzheimer’s
L
Loss of memory
A
Alzheimer’s
…or…
L
Loss of memory
Both are equally valid, since P(A, L) = P(A) P(L|A) = P(L) P(A|L)
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
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The big questions
• How can minds work?
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–
–
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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?
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–
–
–
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
Computing Machinery and Intelligence (1950)
• Considers the question, “Can Machines Think?”
– Too subjective, meaningless – rather, replace this question with an
operational definition of thinking/intelligence
• The Imitation Game
A
Human
Man
B
Woman
Computer
C
Interrogator
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…
小逸李建
(“…To get to the other side.”)
建楼小効
(“That’s funny!”)
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
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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
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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
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–
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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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http://www.darpa.mil/grandchallenge
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.”
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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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