Lambert Schomaker KI2 - 2 Kunstmatige Intelligentie / RuG.
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Transcript of Lambert Schomaker KI2 - 2 Kunstmatige Intelligentie / RuG.
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Lambert Schomaker
KI2 - 2
Kunstmatige Intelligentie / RuG
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Outline
Date 1st hour 2nd hour
6 nov Planning, N&R #11-13
(LS)
idem
13 nov Knowledge-based symbolic methods (LS) #19.6, #21
Example: geometric modeling & matching (MB)
20 nov Statistical symbolic
methods 1 (LS) #17
Example: spam filter
27 nov Statistical symbolic
methods 2 (LS)
Example: autoclass
4 dec Heterogeneous-information integration
Example: writer identification, sat. images
11 dec Grammar induction Articles
18 dec Misc. topics Misc. applications
jan (exam)
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Knowledge-based symbolic methods
Assumption: the Turing / Von Neumann computer is a universal computation engine…
…therefore it can be used at all levels of information processing:
provided an appropriate algorithm can be designed which operates on appropriate representations
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Knowledge-based symbolic methods
provided an appropriate algorithm can be designed…
which operates on appropriate representations…
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Knowledge-based symbolic methods
…provided an appropriate algorithm can be designed…
mechanisms: recursion, hierarchic procedures search algorithms parsers matching algorithms string manipulation.. numerical computing
signal processing image processing statistical processing
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Knowledge-based symbolic methods
…which operates on appropriate representations…
stacks linear strings and arrays matrices linked lists trees
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Knowledge-based symbolic methods
…which operates on appropriate representations…
stacks linear strings and arrays matrices linked lists trees
is indeed succesful in many information processing problems
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Example: double spiral problem
in inner orouter spiral?
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Example: double spiral problem
in inner orouter spiral?
difficult for, e.g., neural nets
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Example: double spiral problem
in inner orouter spiral?
Answer: outside
difficult for, e.g., neural nets
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Example: double spiral problem
in inner orouter spiral?
How?-flood fill algorithm?-other?
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Example: double spiral problem
in inner orouter spiral?
-Find the right representation!
odd/even count
is not sensitive to shape variations of the spiral: a general solution
= Outside
count edges
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Example: double spiral problem
in inner orouter spiral?
Outside
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Culture
If it doesn’t work, you didn’t think hard enough
You have to know what you do
You have to prove that & why it works
Even neural networks work on top of the Turing/von Neumann engine (it will always win)
If you’re smart, you can often avoid NP-completeness
Use of probabilities is a sign of weakness
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Strong points
Scalability is often possible Convenience: little context dependence, no
training Reusability Transformability (compilation) Algorithmic refinement once it is known
how to do a trick (e.g., graphics cards and
DSPs in mobile phones: ugly code but
highly efficient)
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Challenges
Knowledge dependence is expensive– not a problem in “IT” application design– a challenge to AI
Uncertainty
Noise
Brittleness
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Solutions
More and more representational weight: (UML, Semantic Web, XML solves everything)
Symbolic learning mechanisms:– induction: version spaces grammar inference– decision tree learning– rewriting formalisms
Active hypothesis testing (what if…, assume X…)
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Example
In Reading Systems (optical character recognition), only a small part of the algorithm concerns problems of image processing and character classification
Most of the code is concerned with the structure
of the text image:– where are the blobs? – are these blobs text, photo or graphics?– how to segment into meaningful chunks: characters, words?– what is the logical organization (reading order) in the physical
organization of pixels?
Knowledge-based approaches are a necessity!
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Name of conference
Programme committee
Brief description of conference
Submission details
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Example of layout analysis
Knowing the type of a text block strongly reduces the number of possible interpretations
Example: “address block”
Address:– name of person– street, number– postal code, city
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prof dr. L.R.B. SchomakerGrote Appelstraat 239712 TS GroningenNederland
Amsterdam7/7/2003
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address
prof dr. L.R.B. SchomakerGrote Appelstraat 239712 TS GroningenNederland
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address
person name
street
codes+city
country
prof dr. L.R.B. SchomakerGrote Appelstraat 239712 TS GroningenNederland
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address
titles initials surname
street street ,,, digits
4 digits 2 upper case city name
country name
prof dr. L.R.B. SchomakerGrote Appelstraat 239712 TS GroningenNederland
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<address> <person> <title></title> <initials or first name> </initials or first name> <surname></surname> </person> <home> <street name></street name> <number> </number> </home> <city> <postal code> <four digits></four digits> <white space></white space> <two upper-case letters> …. </postal code> </city> <country> </country></address>
(address (title is-left-of initials is-left-of surname) is-above (street name is-left-of number) is-above (city)is-above (country))
Content Layout
prof dr. L.R.B. SchomakerGrote Appelstraat 239712 TS GroningenNederland
etc.
etc.
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<address> <person> <title></title> <initials or first name> </initials or first name> <surname></surname> </person> <home> <street name></street name> <number> </number> </home> <city> <postal code> <four digits></four digits> <white space></white space> <two upper-case letters> …. </postal code> </city> <country> </country></address>
(address (title is-left-of initials is-left-of surname) is-above (street name is-left-of number) is-above (city)is-above (country))
Content Layout
prof dr. L.R.B. SchomakerGrote Appelstraat 239712 TS GroningenNederland
etc.
etc.
HELPS TEXT CLASSIFICATION
HELPS TEXT SEGMENTATION
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