RSGUI with Reverse Prediction

1. RSGUI with Reverse Prediction Julia Johnson Dept of Math and Computer Science Laurentian University, Canada Genevieve Johnson Department of Psychology Grant MacEwan College, Canada

2. Earlier Work Johnson, J.A., P. Campeau. (2005) Reverse Prediction. The Tenth International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2005), Part II. Lecture Notes in Artificial Intelligence. Vol. 3642: 88-97.

3. Reverse Prediction Given decision attribute value v, predict condition attribute values that best imply v. Prediction occurs on the left side of the arrow.

4. Sample Application For Internet business: the given decision attribute: (most customers satisfied) the predicted condition attributes: (characteristics of the product that would lead to most customers being satisfied)

5. Ordinary Prediction Reverse Prediction C1 [given], C2 [given], …, Cn[given]  D[predict] C1 [predict], C2 [predict], …, Cn[predict]  D[given]

6. Ordinary Prediction: C1 [given],C2 [given], … , Cn [given] → D [predict] Reverse Roles of Attributes: D [given] → C1 [predict], C2 [predict], … ,Cn[predict] Reverse Prediction: C1 [predict],C2 [predict], … , Cn [predict] → D [given]

7. Traditional prediction: properties of products given, decision predicted. • Reverse prediction: decision given, properties of products predicted. • Both use the same decision attributes and the same condition attribute.

8. Two properties of attributes: • condition (C) or decision (D), given (G) or predicted (P): • { D, P } → { C, G } • { C, G } → { D, P } • { D, G } → { C, P } • { C, P } → { D, G } • 1 is difficult (not enough information). • 2 is ordinary prediction. • 3 is achieved by reversing the roles of decision and condition attributes. • 4 is reverse prediction.

9. Rough Set Reverse Predicting Algorithm (RSRPA) • Input: a decision table and the decision (most people satisfied) • Output: a set of the predicted best condition attribute values of products that would lead to most people being satisfied.

10. Rough Set Reverse Prediction Algorithm INPUT Decision Table V = Attribute value of concept SETUP • Let U be the universe of objects, C be the set of condition attributes, X be a concept with given value V. • Let BCR =  be a set of decision rules  • C1 = C  /* A rule covers an attribute if the attribute appears in antecedent of the rule.*/

12. Execute Traditional Prediction If more then one rule generated Pick the rule R with the highest coverage BCR = BCR  R For each condition attribute Ci covered by rule R record the pair {(Ci, Cv); C1 = C1 – Ci}; If C1 not =  Execute Traditional Prediction

13. At End The set BCR contains rules that are mutually exclusive with respect to the condition attributes they cover.

14. BCR contains one rule for each condition attribute • No two rules in BCR cover the same attribute

15. Database

17. Evaluation • Hockey Game Application • Information Table Creation • Method of Evaluation • Results

18. Hockey Game Application • Dynamic decision making to test RSRPA • Application is a hockey game • Each team consists of five artificial players. • Condition attributes incorporate information about states.

19. We know the decision attribute (we want our team to win). We want to find the condition attributes (behaviors of the individual team members) that would lead to a win.

20. Hockey Game Application • behaviors are methods coded in Java • Sample behaviors: • A1 - the player chases the puck (Chaser) • A4 - the player predicts how he will get to the puck (Psychic Chaser) • B1 - the player shoots the puck directly on the net (Random Shooter)

21. Hockey Game Application Runs in two modes: • Decision table creation mode • Testing mode

22. Information Table Creation Four states per player: • the puck is in A's possession (mine) • the puck is in A's teammate's possession (mate's) • the puck is in A's opposing team's possession (foe's) • the puck is free ( fate's)

23. Row in the Decision Table

24. Information Table Creation • The first field codes the behavior that the given player uses when he has the puck in his possession (mine) • The second field is the behavior he uses when one of his teammates has the puck (mate's)

25. Information Table Creation • the third field the behavior he uses in state foe's • the fourth field the behavior he uses in state fate's • decision attribute measures the success or failure of a combination of behaviors for each player in each state

26. Method of Evaluation • Enter a team's behaviors • Run that team against hundreds of other randomly generated teams. • Quantitative measures of RSRPA's success obtained by computing the percentage of games won by the team predicted by RSRPA.

27. Results • Rough Set team pitted against 1000 other randomly generated teams. • The Rough Set team won 788 of those 1000 games, lost 144 and tied 68. • Excluding the ties the Rough Set team won 84.5 % of its games.

28. Future Work • Randomly generated teams do not give a fair evaluation of the success of RSRPA. • Use of randomly generated teams assumes a uniform distribution. • But randomly generated teams may exhibit a different distribution (eg normal). • A more representative collection of teams is needed to better test the usefulness of reverse prediction.