1. Lambert Schomaker KI2 - 2 Kunstmatige Intelligentie / RuG

2. 2 Outline Date1 st hour2 nd 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 inductionArticles 18 dec Misc. topicsMisc. applications jan(exam)

3. 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. 4 Knowledge-based symbolic methods  provided an appropriate algorithm can be designed…  which operates on appropriate representations…

5. 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. 6 Knowledge-based symbolic methods  …which operates on appropriate representations…  stacks  linear strings and arrays  matrices  linked lists  trees

7. 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 = Outside count edges

13. Example: double spiral problem in inner or outer spiral? Outside

14. 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. 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. 16 Challenges  Knowledge dependence is expensive –not a problem in “ IT ” application design –a challenge to AI  Uncertainty  Noise  Brittleness

17. 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. 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. 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. prof dr. L.R.B. Schomaker Grote Appelstraat 23 9712 TS Groningen Nederland Amsterdam 7/7/2003

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

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

27. address titles initials surname street street,,, digits 4 digits 2 upper case city name country name prof dr. L.R.B. Schomaker Grote Appelstraat 23 9712 TS Groningen Nederland

28. …. (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. Schomaker Grote Appelstraat 23 9712 TS Groningen Nederland etc.

29. …. (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. Schomaker Grote Appelstraat 23 9712 TS Groningen Nederland etc. HELPS TEXT CLASSIFICATION HELPS TEXT SEGMENTATION