A Class of Algorithms for Distributed Constraint by A. Petcu PDF

By A. Petcu

ISBN-10: 158603989X

ISBN-13: 9781586039899

Multi Agent platforms (MAS) have lately attracted loads of curiosity due to their skill to version many actual existence situations the place details and regulate are allotted between a collection of other brokers. useful functions contain making plans, scheduling, disbursed regulate, source allocation and so forth. a big problem in such structures is coordinating agent judgements, such globally optimum end result is completed. dispensed Constraint Optimization difficulties (DCOP) are a framework that lately emerged as some of the most winning methods to coordination in MAS. a category of Algorithms for allotted Constraint Optimization addresses 3 significant concerns that come up in DCOP: effective optimization algorithms, dynamic and open environments and manipulations from self-interested clients. It makes major contributions in these kinds of instructions through introducing a sequence of DCOP algorithms, that are in accordance with dynamic programming and principally outperform past DCOP algorithms. the root of this classification of algorithms is DPOP, a disbursed set of rules that calls for just a linear variety of messages, therefore incurring low networking overhead. For dynamic environments, self-stabilizing algorithms which may take care of alterations and continually replace their options, are brought. For self clients, the writer proposes the M-DPOP set of rules, that is the 1st DCOP set of rules that makes sincere habit an ex-post Nash equilibrium by way of enforcing the VCG mechanism distributedly. The publication additionally discusses the problem of price range stability and mentions algorithms that let for redistributing (some of) the VCG funds again to the brokers, therefore fending off the welfare loss attributable to losing the VCG taxes.

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Extra info for A Class of Algorithms for Distributed Constraint Optimization

Sample text

We detail in the following how to cast CAs to a DCOP model. Let us assume that agent Ai has bid bi = Ai , Gi , vi . For each good gj ∈ Gi , Ai creates a local variable gji that models the winner of good gj . The domain of this variable is composed of the agents interested in good gj (the ones whose bids contain gj ). Ai connects all variables gji from its local problem with a relation ri that assigns vi only to the combination of values gji = Ai , gki = Ai , . . (the one that assigns all goods gji ∈ bi to Ai ), and 0 to all other combinations.

A1 cannot participate both in M1 and in M3 at the same time, and models this with r11 by assigning −∞ to the combinations which assign the same time slot to M1 and M3 . Furthermore, A1 prefers to hold meeting M3 after M1 , and thus assigns utility 0 to combinations in the upper triangle of r11 , and positive utilities to combinations in the lower triangle of r11 . To ensure that the agents agree on the time slot allocated for each meeting, they must coordinate the assignments of variables in their local subproblems.

Proposition 1 MSP is NP-hard. 2 A similar problem, called Course Scheduling is presented in Zhang and Mackworth [239]. 14 Distributed Constraint Optimization Problems Example 1 (Distributed Meeting Scheduling) Consider an example where 3 agents want to find the optimal schedule for 3 meetings: A1 : {M1 , M3 }, A2 : {M1 , M2 , M3 } and A3 : {M2 , M3 }. There are 3 possible time slots to organize these three meetings: 8AM, 9AM, 10AM . 1 shows how this problem is modeled as a DCOP. 1(a) shows COP1 , the local subproblem of A1 .

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