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