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Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning Martin Mozina, Matej Guid, Jana Krivec, Aleksander Sadikov and Ivan Bratko ECAI 2008 Faculty of Computer and Information Science University of Ljubljana, Slovenia
Motivation for Knowledge Acquisition with Argument Based Machine Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Combining Machine Learning and Expert Knowledge ,[object Object],[object Object],[object Object],IF ... THEN ... IF ... THEN ... ...
Combining Machine Learning and Expert Knowledge ,[object Object],[object Object],[object Object],IF ... THEN ... IF ... THEN ... ...
Combining Machine Learning and Expert Knowledge ,[object Object],[object Object],[object Object],IF ... THEN ... IF ... THEN ... ... ABML
Definition of Argument Based Machine Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],BK,T e i T a i e i
Argument Based Rule Learning ,[object Object],[object Object],[object Object],Name RegularJob Rich AccountStatus HairColor CreditApproved Mr. Bond No Yes Negative Blond Yes Mr. Grey No No Positive Grey No Miss White Yes No Positive Blond Yes Miss Silver Yes Yes Positive Blond Yes Mrs. Brown Yes No Negative Brown No Name RegularJob Rich AccountStatus HairColor CreditApproved Mr. Bond No Yes Negative Blond Yes Mr. Grey No No Positive Grey No Miss White Yes No Positive Blond Yes Miss Silver Yes Yes Positive Blond Yes Mrs. Brown Yes No Negative Brown No Name RegularJob Rich AccountStatus HairColor CreditApproved Mr. Bond No Yes Negative Blond Yes Mr. Grey No No Positive Grey No Miss White Yes No Positive Blond Yes Miss Silver Yes Yes Positive Blond Yes Mrs. Brown Yes No Negative Brown No Name RegularJob Rich AccountStatus HairColor CreditApproved Mr. Bond No Yes Negative Blond Yes Mr. Grey No No Positive Grey No Miss White Yes No Positive Blond Yes Miss Silver Yes Yes Positive Blond Yes Mrs. Brown Yes No Negative Brown No
Formal definition of Argumented Examples ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
ABCN2 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Interactions between expert and ABML ,[object Object],[object Object],[object Object],[object Object],[object Object],Argument ,[object Object],[object Object],ABML critical example learn data set
Interactions between expert and ABML ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Knowledge Acquisition of Chess Concepts used in a Chess Tutoring Application Case Study: Bad Bishop
The Concept of the Bad Bishop ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data set ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],wGM Jana Krivec GM Garry Kasparov FM Matej Guid
Standard M achine L earning M ethods' P erformance with CRAFTY 's features only ,[object Object],[object Object],[object Object],[object Object],Method CA Brier score AUC Decision trees ( C4.5) 73% 0,49 0,74 Logistic regression 70% 0,43 0,84 Rule learning (CN2) 72% 0,39 0,80
First Critical Example ,[object Object],[object Object],[object Object],[object Object],[object Object]
Introducing new attributes into the domain and adding arguments to an example ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
C ounter example ,[object Object],[object Object],[object Object],Counter example : “bad”, although IMPR OVED_BISHOP_MOBILITY is high. ,[object Object],Critical example : “not bad”, IMPR OVED_BISHOP_MOBILITY is high.
I mproving A rguments with Counter Examples ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Assesing “bad” pawns ,[object Object],BAD_PAWNS_AHEAD = 16 + 24 + 2 = 42
After the Final Iteration... ,[object Object],[object Object],[object Object],Attribute Description BAD_PAWNS pawns on the color of the square of the bishop - weighted according to their squares ( bad pawns )  BAD_PAWNS_AHEAD  bad pawns ahead of the bishop BAD_PAWNS _BLOCK_BISHOP_DIAGONAL bad pawns that block the bishop's (front) diagonals   BLOCKED_BAD_PAWNS  bad pawns blocked by opponent's pawns or pieces   IMPROVED_BISHOP_MOBILITY  n umber of squares accessible to the bishop taking into account only pawns of both opponents 
Classification Accuracy Through Iterations Results on the final dataset Method CA Brier score AUC Decision trees ( C4.5) 89% 0,21 0,86 Logistic regression 88% 0,19 0,96 Rule learning (CN2) 88% 0,19 0,94 ABCN2 95% 0,11 0,97
Classification Accuracy Through Iterations ,[object Object],[object Object],A rguments suggested useful attributes AND lead to even more accurate models.
Advantages of ABML for Knowledge Acquisition explain single example easier for experts to articulate knowledge more knowledge from experts critical examples expert provide only relevant knowledge time of experts' involvent is decreased
Advantages of ABML for Knowledge Acquisition counter examples detect deficiencies in expert's explanations even more knowledge from experts arguments constrain learning hypotheses are consistent with expert knowledge hypotheses comprehensible to expert more accurate hypotheses
Conclusions ,[object Object],[object Object],[object Object],[object Object],ABML-based Knowledge Acquisition process provides: A rgument Based Machine Learning enables better knowledge acquisition

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Fighting Knowledge Acquisition Bottleneck with Argument Based ...

  • 1. Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning Martin Mozina, Matej Guid, Jana Krivec, Aleksander Sadikov and Ivan Bratko ECAI 2008 Faculty of Computer and Information Science University of Ljubljana, Slovenia
  • 2.
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  • 12. Knowledge Acquisition of Chess Concepts used in a Chess Tutoring Application Case Study: Bad Bishop
  • 13.
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  • 22. Classification Accuracy Through Iterations Results on the final dataset Method CA Brier score AUC Decision trees ( C4.5) 89% 0,21 0,86 Logistic regression 88% 0,19 0,96 Rule learning (CN2) 88% 0,19 0,94 ABCN2 95% 0,11 0,97
  • 23.
  • 24. Advantages of ABML for Knowledge Acquisition explain single example easier for experts to articulate knowledge more knowledge from experts critical examples expert provide only relevant knowledge time of experts' involvent is decreased
  • 25. Advantages of ABML for Knowledge Acquisition counter examples detect deficiencies in expert's explanations even more knowledge from experts arguments constrain learning hypotheses are consistent with expert knowledge hypotheses comprehensible to expert more accurate hypotheses
  • 26.