Lambert Schomaker

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KI2 - 2

• Lambert Schomaker

Kunstmatige Intelligentie / RuG
2
Outline
Date 1st hour 2nd hour
6 nov Planning, NR 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

8
Example double spiral problem
in inner or outer spiral?
9
Example double spiral problem
in inner or outer spiral?
? difficult for, e.g., neural nets
10
Example double spiral problem
in inner or outer spiral? Answer outside
? difficult for, e.g., neural nets
11
Example double spiral problem

• in inner or
• outer spiral?
• How?
• flood fill algorithm?
• other?

12
Example double spiral problem

• in inner or
• outer spiral?
• Find the right
• representation!
• ? odd/even count
• is not sensitive
• to shape variations
• of the spiral
• a general solution

count edges
Outside
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Example double spiral problem
in inner or outer spiral?
Outside
14
Culture

• If it doesnt work, you didnt 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 youre 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!

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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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pos tze gel
Amsterdam 7/7/2003
Express delivery
prof dr. L.R.B. Schomaker Grote Appelstraat
23 9712 TS Groningen Nederland
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address
prof dr. L.R.B. Schomaker Grote Appelstraat
23 9712 TS Groningen Nederland
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address
prof dr. L.R.B. Schomaker Grote Appelstraat
23 9712 TS Groningen Nederland
person name
street
codescity
country
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address
prof dr. L.R.B. Schomaker Grote Appelstraat
23 9712 TS Groningen Nederland
titles initials surname
street street ,,, digits
4 digits 2 upper case city name
country name
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Content
Layout
ltaddressgt ltpersongt lttitlegtlt/titlegt
ltinitials or first namegt lt/initials
or first namegt ltsurnamegtlt/surnamegt
lt/persongt lthomegt ltstreet
namegtlt/street namegt ltnumbergt lt/numbergt
lt/homegt ltcitygt ltpostal codegt
ltfour digitsgtlt/four digitsgt
ltwhite spacegtlt/white spacegt
lttwo upper-case lettersgt . lt/postal
codegt lt/citygt ltcountrygt
lt/countrygt lt/addressgt
(address (title is-left-of initials
is-left-of surname) is-above (street name
is-left-of number) is-above (city) is-above
(country))
etc.
prof dr. L.R.B. Schomaker Grote Appelstraat
23 9712 TS Groningen Nederland
etc.
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Content
Layout
ltaddressgt ltpersongt lttitlegtlt/titlegt
ltinitials or first namegt lt/initials
or first namegt ltsurnamegtlt/surnamegt
lt/persongt lthomegt ltstreet
namegtlt/street namegt ltnumbergt lt/numbergt
lt/homegt ltcitygt ltpostal codegt
ltfour digitsgtlt/four digitsgt
ltwhite spacegtlt/white spacegt
lttwo upper-case lettersgt . lt/postal
codegt lt/citygt ltcountrygt
lt/countrygt lt/addressgt
(address (title is-left-of initials
is-left-of surname) is-above (street name
is-left-of number) is-above (city) is-above
(country))
etc.
HELPS TEXT SEGMENTATION
prof dr. L.R.B. Schomaker Grote Appelstraat
23 9712 TS Groningen Nederland
etc.
HELPS TEXT CLASSIFICATION
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