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Adopt: asynchronous distributed constraint optimization with quality guarantees
 ARTIFICIAL INTELLIGENCE LABORATORY, MASSACHUSETTS INSTITUTE OF TECHNOLOGY
, 2005
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Distributed Dynamic Backtracking
 In International Joint Conference on AI Workshop on Distributed Constraint Reasoning
, 2001
"... In the scope of distributed constraint reasoning, the main algorithms presented so far have a feature in common: the addition of links between previously unrelated agents, before or during search. This paper presents a new search procedure for finding a solution in a distributed constraint satisfact ..."
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Cited by 60 (2 self)
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In the scope of distributed constraint reasoning, the main algorithms presented so far have a feature in common: the addition of links between previously unrelated agents, before or during search. This paper presents a new search procedure for finding a solution in a distributed constraint satisfaction problem. This algorithm makes use of some of the good properties of centralised dynamic backtracking. It ensures the completeness of search, and allows a high level of asynchronism by sidestepping the unnecessary addition of links. 1.
Open constraint programming
 Artifitial Intelligence
"... Constraint satisfaction and optimization problems often involve multiple participants. For example, producing an automobile involves a supply chain of many companies. Scheduling production, delivery and assembly of the different parts would best be solved as a constraint optimization problem ([35]). ..."
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Cited by 31 (5 self)
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Constraint satisfaction and optimization problems often involve multiple participants. For example, producing an automobile involves a supply chain of many companies. Scheduling production, delivery and assembly of the different parts would best be solved as a constraint optimization problem ([35]). A more familiar task for most of us is meeting scheduling: arrange a set of meetings with varying participants such that no two meetings involving the same person are scheduled at the same time, while respecting order and deadline constraints ([18, 22]). Another application that has been studied in detail is coordinating a network of distributed sensors ([2]). Such problems can of course be solved by gathering all constraints and optimization criteria into a single large CSP, and then solving this problem using a centralized algorithm. In practice there are many cases where this is not feasible, because it is impossible to bound the problem to a manageable set of variables. For example, in meeting scheduling, once two people are planning a common meeting, this meeting is potentially in conflict with many other meetings either of them are planning and whose times are decided in parallel. A centralized solver does not know beforehand
Asynchronous forwardbounding for distributed constraints optimization
 In: Proc. 1st Intern. Workshop on Distributed and Speculative Constraint Processing. (2005
, 2006
"... A new search algorithm for solving distributed constraint optimization problems (DisCOPs) is presented. Agents assign variables sequentially and propagate their assignments asynchronously. The asynchronous forwardbounding algorithm (AFB) is a distributed optimization search algorithm that keeps one ..."
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Cited by 24 (4 self)
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A new search algorithm for solving distributed constraint optimization problems (DisCOPs) is presented. Agents assign variables sequentially and propagate their assignments asynchronously. The asynchronous forwardbounding algorithm (AFB) is a distributed optimization search algorithm that keeps one consistent partial assignment at all times. Forward bounding propagates the bounds on the cost of solutions by sending copies of the partial assignment to all unassigned agents concurrently. The algorithm is described in detail and its correctness proven. Experimental evaluation of AFB on random MaxDisCSPs reveals a phase transition as the tightness of the problem increases. This effect is analogous to the phase transition of MaxCSP when local consistency maintenance is applied [3]. AFB outperforms Synchronous Branch & Bound (SBB) as well as the asynchronous stateoftheart ADOPT algorithm, for the harder problem instances. Both asynchronous algorithms outperform SBB by a large factor. 1
Consistency maintenance for ABT
 In Proc. of CP’2001
, 2001
"... Abstract. One of the most powerful techniques for solving centralized constraint satisfaction problems (CSPs) consists of maintaining local consistency during backtrack search (e.g. [11]). Yet, no work has been reported on such a combination in asynchronous settings 1. The difficulty in this case is ..."
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Cited by 21 (8 self)
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Abstract. One of the most powerful techniques for solving centralized constraint satisfaction problems (CSPs) consists of maintaining local consistency during backtrack search (e.g. [11]). Yet, no work has been reported on such a combination in asynchronous settings 1. The difficulty in this case is that, in the usual algorithms, the instantiation and consistency enforcement steps must alternate sequentially. When brought to a distributed setting, a similar approach forces the search algorithm to be synchronous in order to benefit from consistency maintenance. Asynchronism [24, 14] is highly desirable since it increases flexibility and parallelism, and makes the solving process robust against timing variations. One of the most wellknown asynchronous search algorithms is Asynchronous Backtracking (ABT). This paper shows how an algorithm for maintaining consistency during distributed asynchronous search can be designed upon ABT. The proposed algorithm is complete and has polynomialspace complexity. Since the consistency propagation is optional, this algorithms generalizes forward checking as well as chronological backtracking. An additional advance over existing centralized algorithms is that it can exploit available backtrackingnogoods for increasing the strength of the maintained consistency. The experimental evaluation shows that it can bring substantial gains in computational power compared with existing asynchronous algorithms. 1
Abt with asynchronous reordering
 In IAT
, 2001
"... Existing Distributed Constraint Satisfaction (DisCSP) frameworks can model problems where a)variables and/or b)constraints are distributed among agents. Asynchronous Backtracking (ABT) is the first asynchronous complete algorithm for solving DisCSPs of type a. The order on variables is wellknown as ..."
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Cited by 20 (9 self)
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Existing Distributed Constraint Satisfaction (DisCSP) frameworks can model problems where a)variables and/or b)constraints are distributed among agents. Asynchronous Backtracking (ABT) is the first asynchronous complete algorithm for solving DisCSPs of type a. The order on variables is wellknown as an important issue for constraint satisfaction. Previous polynomial space asynchronous algorithms require for completeness a static order on their variables. We show how agents can asynchronously and concurrently propose reordering in ABT while maintaining the completeness of the algorithm with polynomial space complexity. 1
Solving a distributed CSP with cryptographic multiparty computations, without revealing constraints and without involving trusted servers
"... Everybody has its own constraint satisfaction problem, private concerns that owners prefer to keep as secret as possible. Resources may be shared and cause the need for cooperation. Here we consider the case where privacy is an overwhelming requirement and we assume that a majority of the participa ..."
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Cited by 19 (13 self)
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Everybody has its own constraint satisfaction problem, private concerns that owners prefer to keep as secret as possible. Resources may be shared and cause the need for cooperation. Here we consider the case where privacy is an overwhelming requirement and we assume that a majority of the participants are incorruptible. Namely, given n participants, at least an n/2 unknown subset of them are trustworthy and not corrupted or controlled by attackers. This is a common assumption in cryptographic multiparty computations where techniques exploiting such assumptions are known as threshold schemes. This work shows how a random solution of the described problem can be offered with a secure protocol that does not reveal anything except the existence of the solution and tells each participant the valuations corresponding to its subproblem. The technique is based on the properties of the recent Paillier cryptosystem and needs no external arbiter.
Using additional information in DisCSPs search
 In DCR
, 2004
"... Abstract. A method of volunteering information during asynchronous search on DisCSPs is presented. The meeting scheduling problem (MSP) is formulated as a distributed search problem. In order to implement asynchronous backtracking (ABT) for the MSP, a multivariable version of ABT is described. Agen ..."
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Cited by 18 (3 self)
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Abstract. A method of volunteering information during asynchronous search on DisCSPs is presented. The meeting scheduling problem (MSP) is formulated as a distributed search problem. In order to implement asynchronous backtracking (ABT) for the MSP, a multivariable version of ABT is described. Agents participate in multiple meetings, where each meeting is represented by a variable that needs to be assigned a timeslot. Assignments are constrained by arrivaltime constraints, since meetings take place in different locations. All constraints are local to their agents. Additional information is in the form of Nogoods. During search for a consistent schedule for all meetings, agents can generate and send additional Nogoods to those sent by the ABT algorithm. When additional Nogoods are sent, the efficiency of asynchronous backtracking is enhanced. This effect grows with the number of additional volunteered Nogoods. 1
Asynchronous Forwardchecking for DisCSPs
, 2007
"... A new search algorithm for solving distributed constraint satisfaction problems (DisCSPs) is presented. Agents assign variables sequentially, but perform forward checking asynchronously. The asynchronous forwardchecking algorithm (AFC) is a distributed search algorithm that keeps one consistent par ..."
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Cited by 12 (3 self)
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A new search algorithm for solving distributed constraint satisfaction problems (DisCSPs) is presented. Agents assign variables sequentially, but perform forward checking asynchronously. The asynchronous forwardchecking algorithm (AFC) is a distributed search algorithm that keeps one consistent partial assignment at all times. Forward checking is performed by sending copies of the partial assignment to all unassigned agents concurrently. The algorithm is described in detail and its correctness proven. The sequential assignment method of AFC leads naturally to dynamic ordering of agents during search. Several ordering heuristics are presented. The three best heuristics are evaluated and shown to improve the performance of AFC with static order by a large factor. An experimental comparison of AFC to asynchronous backtracking (ABT) on randomly generated DisCSPs is also presented. AFC with ordering heuristics outperforms ABT by a large factor on the harder instances of random DisCSPs. These results hold for two measures of performance: number of nonconcurrent constraints checks and number of messages sent.
Incentive auctions and stable marriages problems solved with n/2privacy of human preferences
, 2004
"... Incentive auctions let several participants to cooperate for clearing a set of offers and requests, ensuring that each participant cannot do better than by inputing his true utility. This increases the social welfare by efficient allocations, and is proven to have similar outcomes as the traditional ..."
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Cited by 10 (8 self)
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Incentive auctions let several participants to cooperate for clearing a set of offers and requests, ensuring that each participant cannot do better than by inputing his true utility. This increases the social welfare by efficient allocations, and is proven to have similar outcomes as the traditional English Auctions. The deskmates (stable matchings) problem comes from the need of placing students in pairs of two for working in projects or seating in twoseats desks. The stable marriages problem consists of finding matches of a man and a woman out of two sets of men, respectively women. Each of the persons in the previous two problems has a (hopefully stable) secret preference between every two possible partners. The participants want to find an allocation satisfying their secret preferences and without leaking any of these secret preferences, except for what a participant can infer from the identity of the partner/spouse that was recommended to her/him. We use a distributed weighted constraint satisfaction (DisWCSP) framework where the actual constraints are secrets that are not known by any agent. They are defined by a set of functions on some secret inputs from all agents. The solution is also kept secret and each agent learns just the result of applying an agreed function on the solution. The new framework is shown to improve the efficiency in modeling the aforementioned problems. We show how to extend our previous techniques to solve securely problems modeled with the new formalism, and exemplify with the two problems in the title. 1 1