Abt With Asynchronous Reordering

Silaghi, Marius; Sam-Haroud, Djamila; Faltings, Boi

doi:10.1142/9789812811042_0007

Cited by 13 publications

(23 citation statements)

References 2 publications

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“…In the past, different algorithms have been proposed to model diversified computationally intractable problems in the context of DisCSP. Most notably among them is the pioneering work of Yokoo et al [2][3][4], further improved upon by Silaghi et al [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Autonomic Computing-Based Approach to Distributed Constraint Satisfaction Problems

2015

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Distributed constraint satisfaction problems (DisCSPs) are among the widely endeavored problems using agent-based simulation. Fernandez et al. formulated sensor and mobile tracking problem as a DisCSP, known as SensorDCSP In this paper, we adopt a customized ERE (environment, reactive rules and entities) algorithm for the SensorDCSP, which is otherwise proven as a computationally intractable problem. An amalgamation of the autonomy-oriented computing (AOC)-based algorithm (ERE) and genetic algorithm (GA) provides an early solution of the modeled DisCSP. Incorporation of GA into ERE facilitates auto-tuning of the simulation parameters, thereby leading to an early solution of constraint satisfaction. This study further contributes towards a model, built up in the NetLogo simulation environment, to infer the efficacy of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Autonomic Computing-Based Approach to Distributed Constraint Satisfaction Problems

2015

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“…Most existing algorithms for solving DCSPs are extensions of centralized algorithms based on backtracking or iterative improvement with breakout [13,11,14,5,7,9,8]. Their worst case complexity is exponential.…”

Section: Introductionmentioning

confidence: 99%

Multiagent Constraint Satisfaction with Multiply Sectioned Constraint Networks

Xiang

Zhang

2007

Advances in Artificial Intelligence

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Abstract. Variables and constraints in problem domains are often distributed. These distributed constraint satisfaction problems (DCSPs) lend themselves to multiagent solutions. Most existing algorithms for DCSPs are extensions of centralized backtracking or iterative improvement with breakout. Their worst case complexity is exponential. On the other hand, directional consistency based algorithms solve centralized CSPs efficiently if primal graph density is bounded. No known multiagent algorithms solve DCSPs with the same efficiency. We propose the first such algorithm and show that it is sound and complete.

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“…Distributed constraint reasoning, in the form of distributed constraint satisfaction (DisCSP) [27,29,30] and distributed constraint optimization (DCOP) [11,13,15,17], has been offered as a key approach that addresses this problem, as it promises to provide distributed conflict resolution within a collaborating set of agents. Indeed, maintaining privacy is a fundamental motivation in distributed constraint reasoning [13,17,26,29].…”

Section: Introductionmentioning

confidence: 99%

Privacy Loss in Distributed Constraint Reasoning: A Quantitative Framework for Analysis and its Applications

Maheswaran

Pearce

Bowring

et al. 2006

Auton Agent Multi-Agent Syst

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It is critical that agents deployed in real-world settings, such as businesses, offices, universities and research laboratories, protect their individual users' privacy when interacting with other entities. Indeed, privacy is recognized as a key motivating factor in the design of several multiagent algorithms, such as in distributed constraint reasoning (including both algorithms for distributed constraint optimization (DCOP) and distributed constraint satisfaction (DisCSPs)), and researchers have begun to propose metrics for analysis of privacy loss in such multiagent algorithms. Unfortunately, a general quantitative framework to compare these existing metrics for privacy loss or to identify dimensions along which to construct new metrics is currently lacking. This paper presents three key contributions to address this shortcoming. First, the paper presents VPS (Valuations of Possible States), a general quantitative framework to express, analyze and compare existing metrics of privacy loss. Based on a state-space model, VPS is shown to capture various existing measures of privacy created for specific domains of DisCSPs. The utility of VPS is further illustrated through analysis of privacy loss in DCOP algorithms, when such algorithms are used by personal assistant agents to schedule meetings among users. In addition, VPS helps identify dimensions along which to classify and construct new privacy metrics and it also supports their quantitative comparison. Second, the article presents key inference rules that may be used in analysis of privacy loss in DCOP algorithms under different assumptions. Third, detailed experiments based on the VPS-driven analysis lead to the following key results: (i) decentralization by itself does not provide superior protection of privacy in DisCSP/DCOP algorithms when compared with centralization; instead, privacy protection also requires the presence of uncertainty about agents' knowledge of the constraint graph. (ii) one needs to carefully examine the metrics chosen to measure privacy loss; the qualitative properties of privacy loss and hence the conclusions that can be drawn about an algorithm can vary widely based on the metric chosen. This paper should thus serve as a call to arms for further privacy research, particularly within the DisCSP/DCOP arena.

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Abt With Asynchronous Reordering

Cited by 13 publications

References 2 publications

A Hybrid Autonomic Computing-Based Approach to Distributed Constraint Satisfaction Problems

A Hybrid Autonomic Computing-Based Approach to Distributed Constraint Satisfaction Problems

Multiagent Constraint Satisfaction with Multiply Sectioned Constraint Networks

Privacy Loss in Distributed Constraint Reasoning: A Quantitative Framework for Analysis and its Applications

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