Distributed constraint optimization
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Distributed Constraint Optimization. * some slides courtesy of P. Modi http://www.cs.drexel.edu/~pmodi/. Outline. DCOP and real world problems DiMES Algorithms to solve DCOP Synchronous Branch and Bound ADOPT (distributed search) DPOP (dynamic programming) DCPOP Future work.

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Distributed Constraint Optimization

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Distributed constraint optimization

Distributed Constraint Optimization

* some slides courtesy of P. Modi http://www.cs.drexel.edu/~pmodi/



  • DCOP and real world problems

    • DiMES

  • Algorithms to solve DCOP

    • Synchronous Branch and Bound

    • ADOPT (distributed search)

    • DPOP (dynamic programming)

      • DCPOP

  • Future work

Distributed constraint optimization problem dcop

Distributed Constraint Optimization Problem (DCOP)

  • Definition:

    • V = A set of variables

    • Di = A domain of values for Vi

    • U = A set of utility functions on V

  • Goal is to optimize global utility

    • can also model minimal cost problems by using negative utilities

  • One agent per variable

  • Each agent knows Uiwhich is the set of all utility functions that involve Vi



  • Framework for capturing real-world domains involving joint-activities

    • {R1,...,RN} is a set of resources

    • {E1,...,EK} is a set of events

    • Some ∆ st T*∆ = Tlatest – Tearliest and T is a natural number

    • Thus we can characterize the time domain as the set Ŧ = {1,...,T}

    • An event, Ek, is then the tuple (Ak,Lk;Vk) where:

      • Ak is the subset of resources required by the event

      • Lk is the number of contiguous time slots for which the resources Ak are needed

      • Vk denotes the value per time slot of the kth resource in Ak

Dimes cont

DiMES (cont’)

  • It was shown in [Maheswaran et al. 2004] that DiMES can be translated into DCOP

    • Events are mapped to variables

    • The domain for each event is the time slot at which that event will start

    • Utility functions are somewhat complex but were able to be restricted to binary functions

  • It was also shown that several resource allocation problems can be represented in DiMES (including distributed sensor networks)

Synchronous branch and bound

Synchronous Branch and Bound

  • Agents are prioritized into a chain (Hirayama97) or tree

  • Root chooses value, sends to children

  • Children choose value, evaluate partial solution, send partial solution (with cost) to children

  • When cost exceeds upper bound, backtrack

  • Agent explores all its values before reporting to parent

Syncbb example

SyncBB Example



  • Solid line

    • parent/child relationship

  • Dashed line

    • pseudo-parent/pseudo-child relationship

  • Common structure used in search procedures to allow parallel processing of independent branches

  • A node can only have constraints with nodes in the path to root or with descendants



  • SyncBB backtracks only when suboptimality is proven (current solution is greater than an upper bound)

  • ADOPT’s backtrack condition – when lower bound gets too high

    • backtrack before sub-optimality is proven

    • solutions need revisiting

  • Agents are ordered in a Pseudotree

  • Agents concurrently choose values

    • VALUE messages sent down

    • COST messages sent up only to parent

    • THRESHOLD messages sent down only to child

Adopt example

ADOPT Example

  • Suppose parent has two values, “white” and “black”

Distributed constraint optimization


  • Three phase algorithm:

    • Pseudotree generation

    • Utility message propagation bottom-up

    • Optimal value assignments top-down

Dpop phase 1

DPOP: Phase 1

Dpop phase 2

DPOP: Phase 2

  • Propagation starts at leaves, goes up to root

  • Each agent waits for UTIL messages from children

    • does a JOIN

    • sends UTIL message to parent

      • How many total messages in this phase?

Dpop phase 2 cont

DPOP: Phase 2 (cont’)

  • UTIL Message

    • maximum utility for all value combinations of parent/pseudo-parents

    • includes maximum utility values for all children

Dpop phase 3

DPOP: Phase 3

  • Value Propagation

    • After Phase 2, root has a summary view of the global UTIL information

    • Root can then pick the value for itself that gives the best global utility

    • This value is sent to all children

    • Children can now choose their own value, given the value of the root, that optimizes the global utility

    • This process continues until all nodes are assigned a value

Dcop algorithm summary

DCOP Algorithm Summary

  • Adopt

    • distributed search

    • linear size messages

    • worst case exponential number of messages

      • with respect to the depth of the pseudotree

  • DPOP

    • dynamic programming

    • worst case exponential size messages

      • with respect to the induced width of the pseudotree

    • linear number of messages

Can we do better

Can we do better?

  • Are pseudotrees the most efficient translation?

    • The minimum induced width pseudotree is currently the most efficient known translation

    • Finding it is NP-Hard and may require global information

  • Heuristics are used to produce the pseudotrees

    • Current distributed heuristics are all based on some form of DFS or BestFS

    • We prove in a recent paper that pseudotrees produced by these heuristics are suboptimal

Cross edged pseudotrees

Cross-Edged Pseudotrees

  • Pseudotrees that include edges between nodes in separate branches

  • The dashed line is a cross-edge

  • This relaxed form of a pseudotree can produce shorter trees, as well as less overlap between constraints



  • Our extension to DPOP that correctly handles Cross-Edged Pseudotrees

  • We have proved that using an edge-traversal heuristic (DFS, BestFS) it is impossible to produce a traditional pseudotree that outperforms a well chosen cross-edged pseudotree

  • Edge-traversal heuristics are popular because they are easily done in a distributed fashion and require no sharing of global information

Dcpop cont

DCPOP (cont’)

  • Computation size is closer to the minimum induced width than with DPOP

  • Message size can actually be smaller than the minimum induced width

  • A new measurement of sequential path cost (represents the maximal amount of parallelism achieved) also shows improvement

Dcpop metrics

DCPOP Metrics

Future work

Future Work

  • DCOP mapping for a TAEMS based task/resource allocation problem

  • Full integration of uncertainty characteristics into the DCOP model

  • Anytime adaptation with uncertainty

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