GOAL-ORIENTED CONCEPTUALIZATION

OF PROCEDURAL KNOWLEDGEMartin Možina, Matej Guid, Aleksander Sadikov, Vida Groznik, Ivan Bratko

Artificial Intelligence LaboratoryFaculty of Computer and Information Science

University of Ljubljana, Slovenia

ITS 2012

CONCEPTUALIZATION OF PROCEDURAL KNOWLEDGE

ORIGINAL THEORY PROBLEM SOLUTION.......................................................................

axiomslawsformulasrules of the game…

path: requires excessive computation, difficult to memorize

CONCEPTUALIZEDDOMAIN THEORY

DECLARATIVE KNOWLEDGE PROCEDURAL KNOWLEDGE

WHAT? HOW?basic domain knowledge goal-oriented rules

CONCEPTUALIZATION OF PROCEDURAL KNOWLEDGE

ORIGINAL THEORY PROBLEM SOLUTION.......................................................................

axiomslawsformulasrules of the game…

path: requires excessive computation, difficult to memorize

CONCEPTUALIZEDDOMAIN THEORY

DECLARATIVE KNOWLEDGE PROCEDURAL KNOWLEDGE

WHAT? HOW?basic domain knowledge goal-oriented rules

CONCEPTUALIZATION OF PROCEDURAL KNOWLEDGE

ORIGINAL THEORY PROBLEM SOLUTION.......................................................................

axiomslawsformulasrules of the game…

path: requires excessive computation, difficult to memorize

CONCEPTUALIZEDDOMAIN THEORY

basic rules of chesspiece movementsthe 50-move rule…the “right” corner conceptbasic strategy…

procedures: IF-THEN rulessimple and compact ruleseasy to memorize…intuitive knowledgeintermediate goals…

DECLARATIVE KNOWLEDGE PROCEDURAL KNOWLEDGE

PROBLEM STATE SPACE

::

::

::

. . .

. . .

start node

goal nodes

(too) longsolution path

. . .

LEARNING INTERMEDIATE GOALS

::

::

::

. . .

. . .

goal nodes ofintermediate goals

start nodes ofintermediate goals

. . .

KNOWLEDGE ELICITATION WITH ABML

IF ... THEN ...IF ... THEN ......

ABMLargument-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 2008.

arguments

critical examplescounter examples

experts introduce

new concepts (attributes)

human-understandable models

(suitable for teaching)

BENEFITS OF ABML FOR KNOWLEDGE ELICITATION

IF ... THEN ...IF ... THEN ......

ABMLargument-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 2008.

arguments

critical examplescounter examples

GOAL-ORIENTED RULE LEARNING

GOAL EVALUATION:• is the goal achievable?• does the goal always lead to progress?

GOAL-ORIENTED

RULE LEARNING

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?”

GOAL-ORIENTED RULE LEARNING: A “CRITICAL” EXAMPLE

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?”

GOAL-ORIENTED RULE LEARNING: A “CRITICAL” EXAMPLE

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?”

GOAL-ORIENTED RULE LEARNING: A “CRITICAL” EXAMPLE

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.”

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)

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?”

CONCEPTUALIZATION OF DOMAIN KNOWLEDGE: CHESS ENDGAME

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

TEACHING MATERIALS (1): TEXTBOOK INSTRUCTIONS

TEACHING MATERIALS (2): EXAMPLE GAMES WITH GOALS

A GRANDMASTER FAILED TO WIN ...

A grandmaster of chess failed to win the following endgame…

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

GM Kempinski (white) – GM Epishin (black), Bundesliga 2001

A GRANDMASTER FAILED TO WIN ...

… but why our students didn’t?

INTERMEDIATE GOAL: BUILD A BARRIER

INTERMEDIATE GOAL: BUILD A BARRIER

INTERMEDIATE GOAL: BUILD A BARRIER

INTERMEDIATE GOAL: BUILD A BARRIER

INTERMEDIATE GOAL: BUILD A BARRIER

INTERMEDIATE GOAL: BUILD A BARRIER

INTERMEDIATE GOAL: BUILD A BARRIER

INTERMEDIATE GOAL: BUILD A BARRIER

RESULTS OF A PILOT EXPERIMENT

PARTICIPANTS• three chess beginners of different

strengths

TEACHING MATERIALS• goal-oriented textbook instructions• example games with instructions

Phase II: Examination of teaching materialsparticipants were given access to teaching materials

Phase III: Playouts against optimally defending computerafter 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 studyingthe instructions and testing their skill in actual problem solving (playing the endgame).

Phase I: Three trial KBNK gamesparticipants were unable to deliver checkmate

GOAL-ORIENTED CONCEPTUALIZATION OF PROCEDURAL KNOWLEDGE

GOAL-ORIENTED CONCEPTUALIZATION

OF PROCEDURAL KNOWLEDGE

human assimilable simple and compact easily executable

ABML: powerful knowledge elicitation method

next step: ITS for teaching chess endgames

learning intemediate goals

+

? http://www.ailab.si/matej/

dr. Matej Guid. Artificial Intelligence Laboratory, Faculty of Computer and Information Science, University of Ljubljana. Research Page: www.ailab.si/matej