Lambert Schomaker

1. KI2 - 2 Lambert Schomaker Kunstmatige Intelligentie / RuG

2. Outline

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

13. Example: double spiral problem in inner or outer 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!

22. 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

24. Amsterdam 7/7/2003 pos tze gel Express delivery prof dr. L.R.B. Schomaker Grote Appelstraat 23 9712 TS Groningen Nederland

25. address prof dr. L.R.B. Schomaker Grote Appelstraat 23 9712 TS Groningen Nederland

26. address prof dr. L.R.B. Schomaker Grote Appelstraat 23 9712 TS Groningen Nederland person name street codes+city country

27. 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

28. Content Layout <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)) etc. prof dr. L.R.B. Schomaker Grote Appelstraat 23 9712 TS Groningen Nederland etc.

29. Content Layout <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)) etc. HELPS TEXT SEGMENTATION prof dr. L.R.B. Schomaker Grote Appelstraat 23 9712 TS Groningen Nederland etc. HELPS TEXT CLASSIFICATION