lambert schomaker ki2 - 2 kunstmatige intelligentie / rug
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Lambert Schomaker
KI2 - 2
Kunstmatige Intelligentie / RuG
2
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)
3
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
4
Knowledge-based symbolic methods
provided an appropriate algorithm can be designed…
which operates on appropriate representations…
5
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
6
Knowledge-based symbolic methods
…which operates on appropriate representations…
stacks linear strings and arrays matrices linked lists trees
7
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
Example: double spiral problem
in inner orouter spiral?
Example: double spiral problem
in inner orouter spiral?
difficult for, e.g., neural nets
Example: double spiral problem
in inner orouter spiral?
Answer: outside
difficult for, e.g., neural nets
Example: double spiral problem
in inner orouter spiral?
How?-flood fill algorithm?-other?
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
Example: double spiral problem
in inner orouter spiral?
Outside
14
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
15
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)
16
Challenges
Knowledge dependence is expensive– not a problem in “IT” application design– a challenge to AI
Uncertainty
Noise
Brittleness
17
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…)
18
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!
Name of conference
Programme committee
Brief description of conference
Submission details
23
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
prof dr. L.R.B. SchomakerGrote Appelstraat 239712 TS GroningenNederland
Amsterdam7/7/2003
address
prof dr. L.R.B. SchomakerGrote Appelstraat 239712 TS GroningenNederland
address
person name
street
codes+city
country
prof dr. L.R.B. SchomakerGrote Appelstraat 239712 TS GroningenNederland
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
<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.
<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