Preprocessing techniques for accelerating the DCOP algorithm ADOPT

Ali, Syed Muhammad; Koenig, Sven; Tambe, Milind

doi:10.1145/1082473.1082631

Cited by 55 publications

(70 citation statements)

References 20 publications

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“…Targets are located within the area enclosed by the grid. In related studies, models like this were used [6] [5]. In this work, we focus on a type of observation system that consists of autonomous sensor nodes.…”

Section: Grid Modelmentioning

confidence: 99%

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Layered Distributed Constraint Optimization Problem for Resource Allocation Problem in Distributed Sensor Networks

Ota

Matsui

Matsuo

2009

Principles of Practice in Multi-Agent Systems

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Abstract. Distributed sensor network is an important research area of multi-agent systems. We focus on a type of distributed sensor network systems that cooperatively observe multiple targets with multiple autonomous sensors that can control their own view. The problem of allocating observation resource of the distributed sensor network can be formalized as distributed constraint optimization problems. However, in the previous works, the computation cost to solve the resource allocation problem highly increases with its scale/density. In this work, we divide the problem into two layers of problems, and two layered cooperative solvers are applied to those problems. The result of the experiment shows that our proposed method reduces the number of message cycles.

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Section: Grid Modelmentioning

confidence: 99%

“…On the other hand, distributed sensor networks are studied as practical problems of multi-agent systems. In previous studies, resource allocation problems of the distributed sensor network are formalized as DCOPs [5] [6]. There are various purposes of distributed sensor networks.…”

Section: Introductionmentioning

confidence: 99%

Layered Distributed Constraint Optimization Problem for Resource Allocation Problem in Distributed Sensor Networks

Ota

Matsui

Matsuo

2009

Principles of Practice in Multi-Agent Systems

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“…These models differ in the complexity of the computation performed by individual agents and the complexity of the interactions among agents. Distributed Constraint Optimization (DCOP) [31,27,1,38] is a cooperative multiagent coordination framework that has been applied to distributed meeting scheduling, distributed factory and staff scheduling, and sensor network domains [23,30,33,19,37].…”

Section: Introductionmentioning

confidence: 99%

“…While recent advances in efficient DCOP algorithms are encouraging [31,27,38,1,4,46], there are complexities to real domains that are not captured by existing algorithms. This article focuses on the issues that arise from domains that require bounded optimization.…”

Section: Introductionmentioning

confidence: 99%

Balancing local resources and global goals in multiply-constrained DCOP1

Tambe

Yokoo

2010

MGS

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Distributed constraint optimization (DCOP) is a useful framework for cooperative multiagent coordination. DCOP focuses on optimizing a single team objective. However, in many domains, agents must satisfy constraints on resources consumed locally while optimizing the team goal. Yet, these resource constraints may need to be kept private. Designing DCOP algorithms for these domains requires managing complex trade-offs in completeness, scalability, privacy and efficiency.This article defines the multiply-constrained DCOP (MC-DCOP) framework and provides complete (globally optimal) and incomplete (locally optimal) algorithms for solving MC-DCOP problems. Complete algorithms * This paper significantly extends our previous conference paper [3]. It extends that paper by providing all the incomplete algorithms from [2], as well as additional algorithms, more detailed explanation of these new algorithms, additional experimental results and analysis, and a detailed discussion of related work. 1 find the best allocation of scarce resources while optimizing the team objective, while incomplete algorithms are more scalable. The algorithms use four main techniques: (i) transforming constraints to maintain privacy; (ii) dynamically setting upper bounds on resource consumption; (iii) identifying the extent to which the local graph structure allows agents to compute exact bounds; and (iv) using a virtual assignment to flag problems rendered unsatisfiable by resource constraints. Proofs of correctness are presented for all algorithms. Experimental results illustrate the strengths and weaknesses of both the complete and incomplete algorithms.

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“…ADOPT can be described as a parallel version of (Iterative Deepening) A* (Silaghi, Landwehr, & Larrosa, 2004). While DisAO is typically criticized for its significant abandon of the maintenance of the local distribution of the problem at the first conflict (and expensive local computations invalidating the above assumptions as for DPOP (Davin & Modi, 2005;Maheswaran et al, 2004;Ali, Koenig, & Tambe, 2005)), ADOPT can be criticized for its strict message pattern that only provides reduced reasoning opportunities. ADOPT works with orderings on agents dictated by some Depth-First Search tree on the constraint graph, and allows cost communication from an agent only to its parent node.…”

Section: Introductionmentioning

confidence: 99%

ADOPT-ing: unifying asynchronous distributed optimization with asynchronous backtracking

Silaghi

Yokoo

2008

Auton Agent Multi-Agent Syst

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This article presents an asynchronous algorithm for solving Distributed Constraint Optimization problems (DCOPs). The proposed technique unifies asynchronous backtracking (ABT) and asynchronous distributed optimization (ADOPT) where valued nogoods enable more flexible reasoning and more opportunities for communication, leading to an important speed-up. While feedback can be sent in ADOPT by COST messages only to one predefined predecessor, our extension allows for sending such information to any relevant agent. The concept of valued nogood is an extension by Dago and Verfaille of the concept of classic nogood that associates the list of conflicting assignments with a cost and, optionally, with a set of references to culprit constraints.DCOPs have been shown to have very elegant distributed solutions, such as ADOPT, distributed asynchronous overlay (DisAO), or DPOP. These algorithms are typically tuned to minimize the longest causal chain of messages as a measure of how the algorithms will scale for systems with remote agents (with large latency in communication). ADOPT has the property of maintaining the initial distribution of the problem. To be efficient, ADOPT needs a preprocessing step consisting of computing a Depth-First Search (DFS) tree on the constraint graph. Valued nogoods allow for automatically detecting and exploiting the best DFS tree compatible with the current ordering. To exploit such DFS trees it is now sufficient to ensure that they exist. Also, the inference rules available for valued nogoods help to exploit schemes of communication where more feedback is sent to higher priority agents. Together they result in an order of magnitude improvement.

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Preprocessing techniques for accelerating the DCOP algorithm ADOPT

Cited by 55 publications

References 20 publications

Layered Distributed Constraint Optimization Problem for Resource Allocation Problem in Distributed Sensor Networks

Layered Distributed Constraint Optimization Problem for Resource Allocation Problem in Distributed Sensor Networks

Balancing local resources and global goals in multiply-constrained DCOP1

ADOPT-ing: unifying asynchronous distributed optimization with asynchronous backtracking

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