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Goal-Oriented Conceptualization of Procedural Knowledge

Goal-Oriented Conceptualization of Procedural Knowledge. Martin Možina, Matej Guid, Aleksander Sadikov, Vida Groznik, Ivan Bratko. Artificial Intelligence Laboratory Faculty of Computer and Information Science University of Ljubljana, Slovenia ITS 2012.

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Goal-Oriented Conceptualization of Procedural Knowledge

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  1. Goal-Oriented Conceptualization of Procedural Knowledge Martin Možina, Matej Guid, Aleksander Sadikov, Vida Groznik, Ivan Bratko Artificial Intelligence Laboratory Faculty of Computer and Information Science University of Ljubljana, Slovenia ITS 2012

  2. Conceptualization of Procedural Knowledge path: requires excessive computation, difficult to memorize ORIGINAL THEORY • ....................................................................... PROBLEM SOLUTION axioms laws formulas rules of the game … CONCEPTUALIZEDDOMAIN THEORY DECLARATIVE KNOWLEDGE PROCEDURAL KNOWLEDGE WHAT? HOW? basic domain knowledge goal-oriented rules

  3. Conceptualization of Procedural Knowledge path: requires excessive computation, difficult to memorize ORIGINAL THEORY • ....................................................................... PROBLEM SOLUTION axioms laws formulas rules of the game … CONCEPTUALIZEDDOMAIN THEORY DECLARATIVE KNOWLEDGE PROCEDURAL KNOWLEDGE WHAT? HOW? basic domain knowledge goal-oriented rules

  4. Conceptualization of Procedural Knowledge path: requires excessive computation, difficult to memorize ORIGINAL THEORY • ....................................................................... PROBLEM SOLUTION axioms laws formulas rules of the game … CONCEPTUALIZEDDOMAIN THEORY DECLARATIVE KNOWLEDGE PROCEDURAL KNOWLEDGE basic rules of chess piece movements the 50-move rule … the “right” corner concept basic strategy … • procedures: IF-THEN rules • simple and compact rules • easy to memorize • … • intuitive knowledge • intermediate goals • …

  5. Problem State Space start node . . . . . . (too) long solution path : : : : : : . . . goal nodes

  6. Learning Intermediate Goals start nodes of intermediate goals . . . . . . goal nodes of intermediate goals : : : : : : . . .

  7. Knowledge Elicitation with ABML critical examples counter examples IF ... THEN ... IF ... THEN ... ... ABML argument-based machine learning arguments experts’ arguments constrain learning obtained models are consistent with expert knowledge experts introduce new concepts (attributes) human-understandable models (suitable for teaching) Možina M. et al. Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning. ECAI 2008.

  8. Benefits of ABML for Knowledge Elicitation critical examples counter examples IF ... THEN ... IF ... THEN ... ... ABML argument-based machine learning arguments easier for experts to articulate knowledge explain single example expert provides only relevant knowledge “critical” examples detect deficiencies in explanations “counter” examples Možina M. et al. Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning. ECAI 2008.

  9. Goal-Oriented Rule Learning • GOAL EVALUATION: • is the goal achievable? • does the goal always lead to progress? Goal-Oriented Rule Learning

  10. Goal-Oriented Rule Learning: A “Critical” Example Computer (to theexpert): “What goal wouldyou suggest for white in this position? What are the reasons for this goal to apply in this position?”

  11. Goal-Oriented Rule Learning: A “Critical” Example Computer (to theexpert): “What goal wouldyou suggest for white in this position? What are the reasons for this goal to apply in this position?” The expert (a FIDE master): “White can squeeze black king’s area.It is possible to build a barrier and squeeze the area available to the black king.”

  12. Goal-Oriented Rule Learning: A “Critical” Example Computer (to theexpert): “What goal wouldyou suggest for white in this position? What are the reasons for this goal to apply in this position?” The expert (a FIDE master): “White can squeeze black king’s area.It is possible to build a barrier and squeeze the area available to the black king.”

  13. Goal-Oriented Rule Learning: A “Counter” Example Computer found an example where current goal “squeeze black king's area” does not lead to progress. 1.Kf5-g5: mate in 8 moves (optimal execution) 1.Bg4-e2: mate in 10 moves (worst execution) • no progress…. • Computer: • “Would you admonish a student if he or she played 1.Bg4-e2 in this position?” In this case, the expert found this execution of the goal to be perfectly acceptable. XOPTIMAL PLAY • HUMAN-UNDERSTANDABLE PLAY

  14. Conceptualization of Domain Knowledge: Chess Endgame KBNK – the most difficult of elementary chess endgames: several recorded cases when even grandmasters failed to win the result of conceptualization: Hierarchy of (only) 11 GOALS example games with goal-oriented instructions goal-oriented instructions

  15. Teaching Materials (1): Textbook Instructions

  16. Teaching Materials (2): Example Games with Goals

  17. A Grandmaster Failed to Win ... A grandmaster of chess failed to win the following endgame…

  18. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  19. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  20. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  21. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  22. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  23. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  24. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  25. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  26. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  27. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  28. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  29. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  30. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  31. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  32. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  33. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  34. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  35. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  36. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  37. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  38. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  39. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  40. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  41. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  42. A Grandmaster Failed to Win ... GM Kempinski (white) – GM Epishin (black), Bundesliga2001

  43. A Grandmaster Failed to Win ... … but why our students didn’t?

  44. Intermediate Goal: Build a Barrier

  45. Intermediate Goal: Build a Barrier

  46. Intermediate Goal: Build a Barrier

  47. Intermediate Goal: Build a Barrier

  48. Intermediate Goal: Build a Barrier

  49. Intermediate Goal: Build a Barrier

  50. Intermediate Goal: Build a Barrier

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