Strategies for Distributed CBR

1. Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

2. OUTLINE • CBR • Introduction • Applications • Distributed CBR • Autonomous CBR agents • Collaboration policies • Learning to collaborate • Case Bartering • Conclusions

3. What is CBR? • Case Based Reasoning: To solve a new problem by noticing its similarity to one or several previously solved problems and by adapting their solutions instead of working out a solution from scratch.

4. CBR: Cycle New case PROBLEM RETRIEVE General Knowledge CASE BASE RETAIN Tested Case Retrieved cases REUSE REVISE Solved case

5. CBR: Retrieve • Is the key problem in CBR • Indexing: • Relevant features: predictive, discriminatory and explanatory. • Good case representation • Similarity metrics

6. CBR: Reuse • Copy solution (for classification) • Modify old solution: • Reinstantiate (variables) • Local search • Transform • Domain knowledge (causal model)

7. CBR: Revise • Apply solution to problem: • In real world • In simulated world • Evaluate • Repair • User guided • Internal (Domain knowledge)

8. CBR: Retain • What to retain? • Relevant problems • Solution (failed/successful) • Solution method • Store case • Update/indentify indexes

9. CBR systems: • CHEF, cooking planing (Hammon 86) • HYPO, legal reasoning (Ashley/Rissland 87) • PROTOS, medical diagnosis (Bereiss/Porter 88) • CASEY, medial diagnosis (Koton 89) • SAXEX, expresive music (Arcos 96)

10. Distributed CBR • Autonomous CBR agents • Collaboration policies • Learning to collaborate • Case Bartering

11. Autonomous CBR agents • Multiagent CBR system • Each agent has an individual Case-Base • Agent has autonomy in • Problem acquisition • Problem solving • Learning • Collaboration policies • Explicit strategies to improve individual performance

12. Multiagent CBR system • MAC (Multiagent CBR) System • An agent is a pair (Ai,Ci) • A Case base • Classification task in

13. Collaboration policies • Committee • Peer Counsel • Bounded Counsel

14. Collaboration • An agent A1 with problem P • Sends P to some agents A={ A2,…,An } • Each Aj answers with a Solution Endorsing Record (SER) • < { (S1 Ej1)…(Sm Ejm) } , P , Aj > • Endorsing pairs: ( Sk,Ejk )

15. Solution EndorsingRecord (SER) P P1 P2 P3 P4 S1 S1 S2 S3 { (S2,1), (S3,2) } P5 P6 S2 P7 S2 P8 S3 S1 P9 S4 Relevant cases

16. Voting(1) • At is the set of agents submitting SER at time t (including the initiating agent). • Each agent has 1 vote • The vote can be fractionally assigned to various solution classes:

17. Voting(2) • The agent that receives de SERs, aggregates the votes: • The proposed solution is that with maximum Ballot:

18. Committee policy (1) • An agent A1 with problem P • Sends P to all the other agents A={ A2,…,An } • Each Aj answers with a Solution Endorsing Record (SER) • < { (S1 Ej1)…(Sm Ejm) } , P , Aj > • The majority vote selects the solution class

19. Agent interaction: Committee A1 A2 C1 C2 P A3 C3

20. Agent interaction: Committee A1 A2 C1 C2 P P P A3 C3

21. Agent interaction: Committee A1 A2 C1 C2 SER of A2 P SER of A1 A3 C3 SER of A3

22. Agent interaction: Committee A1 A2 C1 C2 P SER of A1 SER of A2 SER of A3 A3 C3 Voting SOLUTION

23. Committee policy (2) • Robust • Good accuracy even with big number of agents with small case-bases • Cost • Linear with number of agents • Always asks all agents

24. Peer Counsel • The agent try to solve the problem by itself • Assess competence of current solution: • Votes for the max class >> rest of votes: • If not competent, ask all other agents • Like committee

25. Agent interaction:Peer Counsel A1 A2 C1 C2 P A3 C3

26. Agent interaction:Peer Counsel A1 A2 C1 C2 P SER of A1 Competent! A3 C3

27. Agent interaction:Peer Counsel A1 A2 C1 C2 P SER of A1 A3 Voting C3 SOLUTION

28. Bounded Counsel • Use self-competence model • If not competent, ask one agent • Agent returns a SER • Assess competence of current solution (Termination Check): • Votes for the max class >> rest of votes: • If not competent, ask one agent more

29. Agent interaction:Bounded Counsel A1 A2 C1 C2 P A3 C3

30. Agent interaction:Bounded Counsel A1 A2 C1 C2 P SER of A1 Not competent! A3 C3

31. Agent interaction:Bounded Counsel A1 A2 C1 C2 P P SER of A1 A3 C3

32. Agent interaction:Bounded Counsel A1 A2 C1 C2 SER of A2 P SER of A1 A3 C3

33. Agent interaction:Bounded Counsel A1 A2 C1 C2 P SER of A1 SER of A2 Competent! A3 C3

34. Agent interaction:Bounded Counsel A1 A2 C1 C2 P SER of A1 SER of A2 A3 Voting C3 SOLUTION

35. Learning to collaborate • Bounded Counsel • fixed predicate to assess competence • Proactive Learning • Improve collaboration • Actions performed in order to learn • learning when to ask counsel

36. Learning to collaborate(2) Do I need the counsel of another agent? A1 C1 P SER of A1 Voting SER of A2 CANDIDATE SOLUTION SER of A3

37. Case Bartering • Why? • Biased individual case-bases • How? • Estimating underlying distribution • Interchanging cases between agents

38. Case Bartering(2) • Estimation of underlying distribution: • Aggregating statistics of all the individual case-bases.

39. Case Bartering(3) • Case-Base bias: • The agents should minimize its bias

40. Case Bartering(4) • Bartering Protocol: • Estimate underlying distribution • Send offers • Confirm offers • Interchange cases • If changes go to 2, else END.

41. Conclusions • Multi-agent systems offer new paradigms to organize AI. • Agent interaction is still a stimulating challenge. • CBR fits perfectly with these kind of applications.