Asymmetric Distributed Constraint Optimization Problems

Abstract

Distributed Constraint Optimization (DCOP) is a powerful framework for representing and solving distributed combinatorial problems, where the variables of the problem are owned by different agents. Many multi-agent problems include constraints that produce different gains (or costs) for the participating agents. Asymmetric gains of constrained agents cannot be naturally represented by the standard DCOP model. The present paper proposes a general framework for Asymmetric DCOPs (ADCOPs). In ADCOPs different agents may have different valuations for constraints that they are involved in. The new framework bridges the gap between multi-agent problems which tend to have asymmetric structure and the standard symmetric DCOP model. The benefits of the proposed model over previous attempts to generalize the DCOP model are discussed and evaluated. Innovative algorithms that apply to the special properties of the proposed ADCOP model are presented in detail. These include complete algorithms that have a substantial advantage in terms of runtime and network load over existing algorithms (for standard DCOPs) which use alternative representations. Moreover, standard incomplete algorithms (i.e., local search algorithms) are inapplicable to the existing DCOP representations of asymmetric constraints and when they are applied to the new ADCOP framework they often fail to converge to a local optimum and yield poor results. The local search algorithms proposed in the present paper converge to high quality solutions. The experimental evidence that is presented reveals that the proposed local search algorithms for ADCOPs achieve high quality solutions while preserving a high level of privacy

Multi-Objective Distributed Constraint Optimization using Semi-Rings

“…Recently, Grinshpoun et al proposed the Asymmetrical DCOP (ADCOP) model [8]. In this model, the utility gained by each agent participating in a constraint is tallied separately, however the objective is to minimize (maximize) the sum of the utilities of all agents.…”

“…The motivation is to preserve the privacy of each agent regarding its local utility. Grinshpoun et al [8] contains a finite set of values which can be assigned to variable X i . R is a set of relations (constraints).…”

“…• ⟨⟨⟨ϵ, {⟨e, 1⟩}, (0, 3)⟩ , ⟨γ, {⟨c, 1⟩}, (1, 2)⟩ , ⟨α, {⟨a, 1⟩}, (0, 3)⟩⟩ , {}⟩ with a total utility of (1,8).…”

“…This is because most DCOP solvers require all agents involved in the constraint must know the details of the constraint. Recent work on asymmetric constraints [8] goes some way towards addressing this concern. By allowing multiple agents to share control of variables, this problem can be modelled naturally.…”

Requests Management for Smartphone-Based Matching Applications Using a Multi-agent Approach

“…Multi-agent systems for solving complex combinatorial problems are often formulated as Distributed Constraint Optimisation Problems (DCOPs) [3,5,6]. These authors propose search algorithms for DCOPs that are analogous to a reactive multi-agent architecture.…”