A distributed complete method for multi agent constraint optimization
Download
1 / 16

A Distributed, Complete Method for Multi-Agent Constraint Optimization - PowerPoint PPT Presentation


  • 112 Views
  • Uploaded on

A Distributed, Complete Method for Multi-Agent Constraint Optimization.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'A Distributed, Complete Method for Multi-Agent Constraint Optimization' - yorick


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
A distributed complete method for multi agent constraint optimization

A Distributed, Complete Method forMulti-Agent Constraint Optimization

Adrian Petcu, Boi Faltingsadrian.petcu@epfl.ch; boi.faltings@epfl.chArtificial Intelligence LaboratorySwiss Federal Institute of Technology (EPFL)IN-Ecublens, 1015 Lausanne, Switzerlandhttp://liawww.epfl.ch/People/apetcu/


Overview
Overview

  • Problem description

  • DTREE – tree propagation algorithm

  • CyPro – utility propagation with cycles

  • CyCopt – splitting the problem

  • Conclusions & future work


Multi agent constraint optimization mcop
Multi-agent Constraint Optimization (MCOP)

  • Constraint Optimization Problems (COP) – tuple <X, D, R>

  • MCOP – multi-agent version of COP: in general, one variable per agent

  • MCOP – solutions have a “quality” to be maximized (constraints are utility functions, not predicates)


Overview1
Overview

  • Problem description

  • DTREE – tree propagation algorithm

  • CyPro – utility propagation with cycles

  • CyCopt – splitting the problem

  • Conclusions & future work


Dtree 1 main ideas
DTREE (1) – main ideas

  • Distributed utility propagation algorithm

  • The leaves of the tree initiate the propagation, and then all nodes forward messages by the “k-1 rule”

  • Nodes terminate after receiving all k messages (k neighbors, one message each)

  • 2 x (n-1) fixed size messages transmitted.


Dtree 2 message dynamics
DTREE (2) – message dynamics

x0

x4

x1

x3

x6

x7

x5

x2

Done!


Dtree 3 computation details
DTREE (3) – computation details

X2X1

X1X0

X3X1

R(X1,X0)


Overview2
Overview

  • Problem description

  • DTREE – tree propagation algorithm

  • CyPro – utility propagation with cycles

  • CyCopt – splitting the problem

  • Conclusions & future work


Cypro 1
CyPro(1)

  • Idea: reduce a complex problem (with cycles) to separate cycle-free parts

  • Choose a set of cycle cut nodes, and initiate propagation from them.

  • Explore all combinations of values of CC nodes, and have them “assemble” the results


Cypro 2
CyPro(2)

“normal” nodes forward by the k-1 rule, and combine contexts

Xj,Xk

Xj,Xk

Xk=vk0

Xj=vj0

Tree parts send messages as before

Cycle cuts act like leaves


Overview3
Overview

  • Problem description

  • DTREE – tree propagation algorithm

  • CyPro – utility propagation with cycles

  • CyCopt – splitting the problem

  • Conclusions & future work


Cycopt 1
CyCOpt(1)

  • Cyclic sub graphs can be processed independently if they are connected through only one CC node.

  • A meta-tree is formed by CC nodes and cyclic sub graphs

  • Processing in the meta-tree proceeds like in DTREE


Cycopt 2
CyCOpt(2)

Xj

Xk


Overview4
Overview

  • Problem description

  • DTREE – tree propagation algorithm

  • CyPro – utility propagation with cycles

  • CyCopt – splitting the problem

  • Conclusions & future work


Conclusions future work
Conclusions & future work

  • Linear message size, linear memory

  • Reduction in complexity from domn >> dom|CC|>> dom|CCs in largest subgraph|

  • Choice of the cycle cutset is important

  • Exhaustive search within a cyclic subgraph is maybe not necessary


References
References

  • F. Kschischang, B.Frey, and H. Loeliger. Factor graphs and the sum-product algorithm. IEEE TRANSACTIONS ON INFORMATION THEORY, 2001.

  • Rina Dechter. Constraint Processing. Morgan Kaufmann, 2003.

  • P. Modi, W. Shen, M. Tambe, and M. Yokoo. An asynchronous complete method for distributed constraint optimization, 2003.