Annals of Mathematics and Artificial Intelligence, Apr 20, 2021
Privacy has traditionally been a major motivation of distributed problem solving. One popular app... more Privacy has traditionally been a major motivation of distributed problem solving. One popular approach to enable privacy in distributed environments is to implement complex cryptographic protocols. In this paper, we propose a different, orthogonal approach, which is to control the quality and the quantity of publicized data. We consider the Open Constraint Programming model and focus on algorithms that solve Distributed Constraint Optimization Problems (DCOPs) using a local search approach. Two such popular algorithms exist to find good solutions to DCOP: DSA and GDBA. In this paper, we propose DSAB, a new algorithm that merges ideas from both algorithms to allow extensive handling of constraint privacy. We also study how algorithms behave when solving Utilitarian DCOPs, where utilitarian agents want to reach an agreement while reducing the privacy loss. We experimentally study how the utilitarian approach impacts the quality of the solution and of publicized data.
Annals of Mathematics and Artificial Intelligence, 2021
Privacy has traditionally been a major motivation of distributed problem solving. One popular app... more Privacy has traditionally been a major motivation of distributed problem solving. One popular approach to enable privacy in distributed environments is to implement complex cryptographic protocols. In this paper, we propose a different, orthogonal approach, which is to control the quality and the quantity of publicized data. We consider the Open Constraint Programming model and focus on algorithms that solve Distributed Constraint Optimization Problems (DCOPs) using a local search approach. Two such popular algorithms exist to find good solutions to DCOP: DSA and GDBA. In this paper, we propose DSAB, a new algorithm that merges ideas from both algorithms to allow extensive handling of constraint privacy. We also study how algorithms behave when solving Utilitarian DCOPs, where utilitarian agents want to reach an agreement while reducing the privacy loss. We experimentally study how the utilitarian approach impacts the quality of the solution and of publicized data.
Privacy has traditionally been a major motivation of distributed problem solving. In this paper, ... more Privacy has traditionally been a major motivation of distributed problem solving. In this paper, we focus on privacy issues when solving Distributed Constraint Optimization Problems (DCOPs) using a local search approach. Two such popular algorithms exist to find good solutions to DCOP: DSA and GDBA. However, these were not designed with privacy in mind. In this paper, we propose DSAB, a new algorithm that merges ideas from both algorithms to allow extensive handling of constraint privacy. We also study how algorithms behave when solving Utilitarian DCOPs, where utilitarian agents want to reach an agreement while reducing the privacy loss. We show experimentally that this allows us reductions of domain privacy loss by a factor 2 to 3 with no significant impact on the quality of the solution.
Real life problems can be solved by a distributed way, in particular by multi-agent approaches. H... more Real life problems can be solved by a distributed way, in particular by multi-agent approaches. However, the fault tolerance is not guarantee when an agent, for example, does not have any activity (e.g. it dies). This problem is very crucial, when the interactional model is based on a Distributed CSP. Many algorithms have been proposed in the literature, but they give wrong results if an agent dies. This paper presents an approach which is based on a replication principle: each local CSP is replicated in another agent.
Privacy has traditionally been a major motivation for distributed problem solving. Distributed Co... more Privacy has traditionally been a major motivation for distributed problem solving. Distributed Constraint Satisfaction Problem (DisCSP) as well as Distributed Constraint Optimization Problem (DCOP) are fundamental models used to solve various families of distributed problems. Even though several approaches have been proposed to quantify and preserve privacy in such problems, none of them is exempt from limitations. Here we approach the problem by assuming that computation is performed among utilitarian agents. We introduce a utilitarian approach where the utility of each state is estimated as the difference between the reward for reaching an agreement on assignments of shared variables and the cost of privacy loss. We investigate extensions to solvers where agents integrate the utility function to guide their search and decide which action to perform, defining thereby their policy. We show that these extended solvers succeed in significantly reducing privacy loss without significant...
Les consistances sont des propriétés de réseaux de contraintes qui peuvent être exploitées afin d... more Les consistances sont des propriétés de réseaux de contraintes qui peuvent être exploitées afin de générer des inférences. Lorsqu’un nombre important d’inférences peut être effectué, il devient alors plus facile de résoudre les réseaux à l’aide par exemple d’une recherche systématique. Dans ce papier, nous nous intéressons aux consistances de relation, i.e. aux consistances qui permettent d’identifier des couples de valeurs inconsistantes. Nous proposons une nouvelle consistance, appelée consistance duale (DC pour Dual Consistency), et nous la comparons à la consistance de chemin (PC pour Path Consistency). Nous montrons que la DC conservative (CDC), i.e. DC telle que seules les relations associées aux contraintes du réseau soient filtrées, est
Annals of Mathematics and Artificial Intelligence, Apr 20, 2021
Privacy has traditionally been a major motivation of distributed problem solving. One popular app... more Privacy has traditionally been a major motivation of distributed problem solving. One popular approach to enable privacy in distributed environments is to implement complex cryptographic protocols. In this paper, we propose a different, orthogonal approach, which is to control the quality and the quantity of publicized data. We consider the Open Constraint Programming model and focus on algorithms that solve Distributed Constraint Optimization Problems (DCOPs) using a local search approach. Two such popular algorithms exist to find good solutions to DCOP: DSA and GDBA. In this paper, we propose DSAB, a new algorithm that merges ideas from both algorithms to allow extensive handling of constraint privacy. We also study how algorithms behave when solving Utilitarian DCOPs, where utilitarian agents want to reach an agreement while reducing the privacy loss. We experimentally study how the utilitarian approach impacts the quality of the solution and of publicized data.
Annals of Mathematics and Artificial Intelligence, 2021
Privacy has traditionally been a major motivation of distributed problem solving. One popular app... more Privacy has traditionally been a major motivation of distributed problem solving. One popular approach to enable privacy in distributed environments is to implement complex cryptographic protocols. In this paper, we propose a different, orthogonal approach, which is to control the quality and the quantity of publicized data. We consider the Open Constraint Programming model and focus on algorithms that solve Distributed Constraint Optimization Problems (DCOPs) using a local search approach. Two such popular algorithms exist to find good solutions to DCOP: DSA and GDBA. In this paper, we propose DSAB, a new algorithm that merges ideas from both algorithms to allow extensive handling of constraint privacy. We also study how algorithms behave when solving Utilitarian DCOPs, where utilitarian agents want to reach an agreement while reducing the privacy loss. We experimentally study how the utilitarian approach impacts the quality of the solution and of publicized data.
Privacy has traditionally been a major motivation of distributed problem solving. In this paper, ... more Privacy has traditionally been a major motivation of distributed problem solving. In this paper, we focus on privacy issues when solving Distributed Constraint Optimization Problems (DCOPs) using a local search approach. Two such popular algorithms exist to find good solutions to DCOP: DSA and GDBA. However, these were not designed with privacy in mind. In this paper, we propose DSAB, a new algorithm that merges ideas from both algorithms to allow extensive handling of constraint privacy. We also study how algorithms behave when solving Utilitarian DCOPs, where utilitarian agents want to reach an agreement while reducing the privacy loss. We show experimentally that this allows us reductions of domain privacy loss by a factor 2 to 3 with no significant impact on the quality of the solution.
Real life problems can be solved by a distributed way, in particular by multi-agent approaches. H... more Real life problems can be solved by a distributed way, in particular by multi-agent approaches. However, the fault tolerance is not guarantee when an agent, for example, does not have any activity (e.g. it dies). This problem is very crucial, when the interactional model is based on a Distributed CSP. Many algorithms have been proposed in the literature, but they give wrong results if an agent dies. This paper presents an approach which is based on a replication principle: each local CSP is replicated in another agent.
Privacy has traditionally been a major motivation for distributed problem solving. Distributed Co... more Privacy has traditionally been a major motivation for distributed problem solving. Distributed Constraint Satisfaction Problem (DisCSP) as well as Distributed Constraint Optimization Problem (DCOP) are fundamental models used to solve various families of distributed problems. Even though several approaches have been proposed to quantify and preserve privacy in such problems, none of them is exempt from limitations. Here we approach the problem by assuming that computation is performed among utilitarian agents. We introduce a utilitarian approach where the utility of each state is estimated as the difference between the reward for reaching an agreement on assignments of shared variables and the cost of privacy loss. We investigate extensions to solvers where agents integrate the utility function to guide their search and decide which action to perform, defining thereby their policy. We show that these extended solvers succeed in significantly reducing privacy loss without significant...
Les consistances sont des propriétés de réseaux de contraintes qui peuvent être exploitées afin d... more Les consistances sont des propriétés de réseaux de contraintes qui peuvent être exploitées afin de générer des inférences. Lorsqu’un nombre important d’inférences peut être effectué, il devient alors plus facile de résoudre les réseaux à l’aide par exemple d’une recherche systématique. Dans ce papier, nous nous intéressons aux consistances de relation, i.e. aux consistances qui permettent d’identifier des couples de valeurs inconsistantes. Nous proposons une nouvelle consistance, appelée consistance duale (DC pour Dual Consistency), et nous la comparons à la consistance de chemin (PC pour Path Consistency). Nous montrons que la DC conservative (CDC), i.e. DC telle que seules les relations associées aux contraintes du réseau soient filtrées, est
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