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Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

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Page 1: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Strategies for Distributed CBR

Santi Ontañón

IIIA-CSIC

Page 2: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

OUTLINE

CBR Introduction Applications

Distributed CBR Autonomous CBR agents Collaboration policies Learning to collaborate Case Bartering

Conclusions

Page 3: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

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.

Page 4: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

GeneralKnowledge

CBR: Cycle

CASEBASE Retrieved

cases

Newcase

TestedCase

Solvedcase

RETRIEVE

REUSEREVISE

RETAIN

PROBLEM

Page 5: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

CBR: Retrieve

Is the key problem in CBR

Indexing:

Relevant features: predictive, discriminatory

and explanatory.

Good case representation

Similarity metrics

Page 6: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

CBR: Reuse

Copy solution (for classification)

Modify old solution: Reinstantiate (variables)

Local search

Transform Domain knowledge (causal model)

Page 7: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

CBR: Revise

Apply solution to problem: In real world In simulated world

Evaluate Repair

User guided Internal (Domain knowledge)

Page 8: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

CBR: Retain

What to retain? Relevant problems

Solution (failed/successful)

Solution method

Store case Update/indentify indexes

Page 9: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

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)

Page 10: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Distributed CBR

Autonomous CBR agents

Collaboration policies

Learning to collaborate

Case Bartering

Page 11: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

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

Page 12: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Multiagent CBR system

MAC (Multiagent CBR) System

An agent is a pair (Ai,Ci)

A Case base

Classification task in

{(Pji,Sk)}j=1...Ni

{(Ai,Ci)}i=1...n

{S1,...,SK}

Page 13: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Collaboration policies

Committee

Peer Counsel

Bounded Counsel

Page 14: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

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 Ej

m) } , P , Aj >

Endorsing pairs: ( Sk,Ejk )

Page 15: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Solution EndorsingRecord (SER)

P4

S3

P1

S1

P3

S2

P5

S2

P8

S1

P7

S3

P2

S1

P6

S2

P9

S4

{ (S2,1), (S3,2) }

P

Relevant cases

Page 16: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

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:

Vote(Sk,Ai) =Ek

i

c+ Eki

r=1...K∑

Page 17: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Voting(2)

The agent that receives de SERs, aggregates the votes:

The proposed solution is that with maximum Ballot:

Ballott(Sk,At)= Vote(Sk,Ai)

Ai ∈At

Solt(P,At) =argmaxk=1...K

Ballott(Sk,At)( )

Page 18: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

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 Ej

m) } , P , Aj >

The majority vote selects the solution class

Page 19: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Agent interaction: Committee

A1 C1

P

A3 C3

A2 C2

Page 20: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Agent interaction: Committee

A1 C1

P

A3 C3

A2 C2P

P

Page 21: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Agent interaction: Committee

A1 C1

P SER of A1

A3 C3

A2 C2SER of A2

SER of A3

Page 22: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Agent interaction: Committee

A1 C1

P SER of A1

SER of A2

SOLUTION

SER of A3

A3 C3

A2 C2

Voting

Page 23: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

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

Page 24: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

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

Vmaxt

Vrestt >η

Page 25: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Agent interaction:Peer Counsel

A1 C1

P

A3 C3

A2 C2

Page 26: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Agent interaction:Peer Counsel

A1 C1

P

A3 C3

A2 C2

SER of A1

Competent!

Page 27: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Agent interaction:Peer Counsel

A1 C1

P

A3 C3

A2 C2

SER of A1

SOLUTION

Voting

Page 28: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

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

Vmaxt

Vrestt >η

Page 29: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Agent interaction:Bounded Counsel

A1 C1

P

A3 C3

A2 C2

Page 30: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Agent interaction:Bounded Counsel

A1 C1

P SER of A1

A3 C3

A2 C2

Not competent!

Page 31: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Agent interaction:Bounded Counsel

A1 C1

P SER of A1

A3 C3

A2 C2P

Page 32: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Agent interaction:Bounded Counsel

A1 C1

P SER of A1

A3 C3

A2 C2SER of A2

Page 33: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Agent interaction:Bounded Counsel

A1 C1

P SER of A1

A3 C3

A2 C2

SER of A2

Competent!

Page 34: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Agent interaction:Bounded Counsel

A1 C1

P SER of A1

A3 C3

A2 C2

SER of A2

SOLUTION

Voting

Page 35: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

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

Page 36: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Learning to collaborate(2)

A1 C1

P SER of A1

SER of A2

SER of A3

Do I need thecounsel of

another agent?

CANDIDATESOLUTION

Voting

Page 37: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Case Bartering

Why? Biased individual case-bases

How? Estimating underlying distribution

Interchanging cases between agents

Page 38: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Case Bartering(2)

Estimation of underlying distribution: Aggregating statistics of all the individual

case-bases.

D j =dij

i=1

n∑dil

l=1

K∑i=1

n∑

D={D1,...,DK}

Page 39: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Case Bartering(3)

Case-Base bias:

The agents should minimize its bias

Bias(Ai) = Dl −dil

dij

j=1

K∑

⎜ ⎜ ⎜

⎟ ⎟ ⎟

2

l=1

K

Page 40: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

Case Bartering(4)

Bartering Protocol:

1. Estimate underlying distribution

2. Send offers

3. Confirm offers

4. Interchange cases

5. If changes go to 2, else END.

Page 41: Strategies for Distributed CBR Santi Ontañón IIIA-CSIC

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.