G OAL -O RIENTED C ONCEPTUALIZATION OF P ROCEDURAL K NOWLEDGE 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

C ONCEPTUALIZATION OF P ROCEDURAL K NOWLEDGE ORIGINAL THEORYPROBLEM SOLUTION axioms laws formulas rules of the game … path: requires excessive computation, difficult to memorize CONCEPTUALIZED DOMAIN THEORY DECLARATIVE KNOWLEDGEPROCEDURAL KNOWLEDGE basic domain knowledgegoal-oriented rules

C ONCEPTUALIZATION OF P ROCEDURAL K NOWLEDGE ORIGINAL THEORYPROBLEM SOLUTION axioms laws formulas rules of the game … path: requires excessive computation, difficult to memorize CONCEPTUALIZED DOMAIN THEORY DECLARATIVE KNOWLEDGEPROCEDURAL KNOWLEDGE basic domain knowledgegoal-oriented rules

C ONCEPTUALIZATION OF P ROCEDURAL K NOWLEDGE ORIGINAL THEORYPROBLEM SOLUTION axioms laws formulas rules of the game … path: requires excessive computation, difficult to memorize CONCEPTUALIZED DOMAIN THEORY 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 … DECLARATIVE KNOWLEDGEPROCEDURAL KNOWLEDGE

P ROBLEM S TATE S PACE :::: :::: ::::... start node goal nodes (too) long solution path...

L EARNING I NTERMEDIATE G OALS :::: :::: ::::... goal nodes of intermediate goals start nodes of intermediate goals...

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

B ENEFITS OF ABML FOR K NOWLEDGE E LICITATION IF... THEN ABML argument-based machine learning explain single example easier for experts to articulate knowledge “critical” examples expert provides only relevant knowledge “counter” examples detect deficiencies in explanations Možina M. et al. Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning. ECAI arguments critical examples counter examples

G OAL -O RIENTED R ULE L EARNING GOAL EVALUATION: is the goal achievable? does the goal always lead to progress? G OAL -O RIENTED R ULE L EARNING

Computer (to the expert): “What goal would you suggest for white in this position? What are the reasons for this goal to apply in this position?” G OAL -O RIENTED R ULE L EARNING : A “C RITICAL ” E XAMPLE

Computer (to the expert): “What goal would you suggest for white in this position? What are the reasons for this goal to apply in this position?” G OAL -O RIENTED R ULE L EARNING : A “C RITICAL ” E XAMPLE 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.”

Computer (to the expert): “What goal would you suggest for white in this position? What are the reasons for this goal to apply in this position?” G OAL -O RIENTED R ULE L EARNING : A “C RITICAL ” E XAMPLE 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.”

G OAL -O RIENTED R ULE L EARNING : A “C OUNTER ” E XAMPLE 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) In this case, the expert found this execution of the goal to be perfectly acceptable. HUMAN-UNDERSTANDABLE PLAY X OPTIMAL PLAY no progress…. Computer: “Would you admonish a student if he or she played 1.Bg4-e2 in this position?”

C ONCEPTUALIZATION OF D OMAIN K NOWLEDGE : C HESS E NDGAME goal-oriented instructions example games with goal-oriented instructions 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

T EACHING M ATERIALS (1): T EXTBOOK I NSTRUCTIONS

T EACHING M ATERIALS (2): E XAMPLE G AMES WITH G OALS

A G RANDMASTER F AILED TO W IN... A grandmaster of chess failed to win the following endgame…

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A G RANDMASTER F AILED TO W IN... … but why our students didn’t?

I NTERMEDIATE G OAL : B UILD A B ARRIER

R ESULTS OF A P ILOT E XPERIMENT PARTICIPANTS three chess beginners of different strengths TEACHING MATERIALS goal-oriented textbook instructions example games with instructions Phase II: Examination of teaching materials participants were given access to teaching materials Phase III: Playouts against optimally defending computer after each game (but not during the games!) the materials were accessible to participants The students learned the skill operationally in up to an hour’s time of studying the instructions and testing their skill in actual problem solving (playing the endgame). Phase I: Three trial KBNK games participants were unable to deliver checkmate

G OAL -O RIENTED C ONCEPTUALIZATION OF P ROCEDURAL K NOWLEDGE human assimilable simple and compact easily executable ABML: powerful knowledge elicitation method next step: ITS for teaching chess endgames learning intemediate goals +

dr. Matej Guid. Artificial Intelligence Laboratory, Faculty of Computer and Information Science, University of Ljubljana. Research Page: