1 / 16

A Distributed, Complete Method for Multi-Agent Constraint Optimization

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

yorick
Download Presentation

A Distributed, Complete Method for Multi-Agent Constraint Optimization

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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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/

  2. Overview • Problem description • DTREE – tree propagation algorithm • CyPro – utility propagation with cycles • CyCopt – splitting the problem • Conclusions & future work

  3. 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)

  4. Overview • Problem description • DTREE – tree propagation algorithm • CyPro – utility propagation with cycles • CyCopt – splitting the problem • Conclusions & future work

  5. 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.

  6. DTREE (2) – message dynamics x0 x4 x1 x3 x6 x7 x5 x2 Done!

  7. DTREE (3) – computation details X2X1 X1X0 X3X1 R(X1,X0)

  8. Overview • Problem description • DTREE – tree propagation algorithm • CyPro – utility propagation with cycles • CyCopt – splitting the problem • Conclusions & future work

  9. 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

  10. 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

  11. Overview • Problem description • DTREE – tree propagation algorithm • CyPro – utility propagation with cycles • CyCopt – splitting the problem • Conclusions & future work

  12. 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

  13. CyCOpt(2) Xj Xk

  14. Overview • Problem description • DTREE – tree propagation algorithm • CyPro – utility propagation with cycles • CyCopt – splitting the problem • Conclusions & future work

  15. 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

  16. 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.

More Related