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33
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
Odpop: An algorithm for open/distributed constraint optimization
 In AAAI
, 2006
"... Abstract. We propose ODPOP, a new distributed algorithm for open multiagent combinatorial optimization [3]. The ODOP algorithm explores the same search space as the dynamic programming algorithm DPOP [10] or the AND/OR search algorithm AOBB [2], but does so in an incremental, bestfirst fashion suit ..."
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Cited by 28 (5 self)
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Abstract. We propose ODPOP, a new distributed algorithm for open multiagent combinatorial optimization [3]. The ODOP algorithm explores the same search space as the dynamic programming algorithm DPOP [10] or the AND/OR search algorithm AOBB [2], but does so in an incremental, bestfirst fashion suitable for open problems. ODPOP has several advantages over DPOP. First, it uses messages whose size only grows linearly with the treewidth of the problem. Second, by letting agents explore values in a nonincreasing order of preference, it saves a significant amount of messages and computation over the basic DPOP algorithm. To show the merits of our approach, we report on experiments with practically sized distributed meeting scheduling problems in a multiagent system. 1
Fairness with an honest minority and a rational majority. Cryptology ePrint Archive, Report 2008/097
, 2008
"... Abstract. We provide a simple protocol for secret reconstruction in any threshold secret sharing scheme, and prove that it is fair when executed with many rational parties together with a small minority of honest parties. That is, all parties will learn the secret with high probability when the hone ..."
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Cited by 16 (3 self)
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Abstract. We provide a simple protocol for secret reconstruction in any threshold secret sharing scheme, and prove that it is fair when executed with many rational parties together with a small minority of honest parties. That is, all parties will learn the secret with high probability when the honest parties follow the protocol and the rational parties act in their own selfinterest (as captured by a setNash analogue of trembling hand perfect equilibrium). The protocol only requires a standard (synchronous) broadcast channel, tolerates both early stopping and incorrectly computed messages, and only requires 2 rounds of communication. Previous protocols for this problem in the cryptographic or economic models have either required an honest majority, used strong communication channels that enable simultaneous exchange of information, or settled for approximate notions of security/equilibria. They all also required a nonconstant number of rounds of communication.
MBDPOP: A new memorybounded algorithm for distributed optimization
 In Proceedings of IJCAI
, 2007
"... In distributed combinatorial optimization problems, dynamic programming algorithms like DPOP ([Petcu and Faltings, 2005]) require only a linear number of messages, thus generating low communication overheads. However, DPOP’s memory requirements are exponential in the induced width of the constraint ..."
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Cited by 14 (4 self)
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In distributed combinatorial optimization problems, dynamic programming algorithms like DPOP ([Petcu and Faltings, 2005]) require only a linear number of messages, thus generating low communication overheads. However, DPOP’s memory requirements are exponential in the induced width of the constraint graph, which may be prohibitive for problems with large width. We present MBDPOP, a new hybrid algorithm that can operate with bounded memory. In areas of low width, MBDPOP operates like standard DPOP (linear number of messages). Areas of high width are explored with bounded propagations using the idea of cyclecuts [Dechter, 2003]. We introduce novel DFSbased mechanisms for determining the cyclecutset, and for grouping cyclecut nodes into clusters. We use caching between clusters to reduce the complexity to exponential in the largest number of cycle cuts in a single cluster. We compare MBDPOP with ADOPT [Modi et al., 2005], the current state of the art in distributed search with bounded memory. MBDPOP consistently outperforms ADOPT on 3 problem domains, with respect to 3 metrics, providing speedups of up to 5 orders of magnitude. 1
PCDPOP: A new partial centralization algorithm for distributed optimization
 In Proceedings of the 20th International Joint Conference on Artificial Intelligence, IJCAI07
, 2007
"... Fully decentralized algorithms for distributed constraint optimization often require excessive amounts of communication when applied to complex problems. The OptAPO algorithm of [Mailler and Lesser, 2004] uses a strategy of partial centralization to mitigate this problem. We introduce PCDPOP, a new ..."
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Cited by 12 (3 self)
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Fully decentralized algorithms for distributed constraint optimization often require excessive amounts of communication when applied to complex problems. The OptAPO algorithm of [Mailler and Lesser, 2004] uses a strategy of partial centralization to mitigate this problem. We introduce PCDPOP, a new partial centralization technique, based on the DPOP algorithm of [Petcu and Faltings, 2005]. PCDPOP provides better control over what parts of the problem are centralized and allows this centralization to be optimal with respect to the chosen communication structure. Unlike OptAPO, PCDPOP allows for a priory, exact predictions about privacy loss, communication, memory and computational requirements on all nodes and links in the network. Upper bounds on communication and memory requirements can be specified. We also report strong efficiency gains over OptAPO in experiments on three problem domains. 1
PrivacyPreserving Multiagent Constraint Satisfaction
 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING
, 2009
"... Constraint satisfaction has been a very successful paradigm for solving problems such as resource allocation and planning. Many of these problems pose themselves in a context involving multiple agents, and protecting privacy of information among them is often desirable. Secure multiparty computation ..."
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Cited by 9 (5 self)
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Constraint satisfaction has been a very successful paradigm for solving problems such as resource allocation and planning. Many of these problems pose themselves in a context involving multiple agents, and protecting privacy of information among them is often desirable. Secure multiparty computation (SMC) provides methods that in principle allow such computation without leaking any information. However, it does not consider the issue of keeping agents’ decisions private from one another. In this paper, we show an algorithm that uses SMC in distributed computation to satisfy this objective.
Auctions and Bidding: A Guide for Computer Scientists
"... There is a veritable menagerie of auctions — singledimensional, multidimensional, singlesided, ..."
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Cited by 9 (0 self)
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There is a veritable menagerie of auctions — singledimensional, multidimensional, singlesided,
Simple Negotiation Schemes for Agents with Simple Preferences: Sufficiency, Necessity and Maximality
 JOURNAL OF AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS
, 2009
"... We investigate the properties of an abstract negotiation framework where agents autonomously negotiate over allocations of indivisible resources. In this framework, reaching an allocation that is optimal may require very complex multilateral deals. Therefore, we are interested in identifying classe ..."
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Cited by 7 (5 self)
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We investigate the properties of an abstract negotiation framework where agents autonomously negotiate over allocations of indivisible resources. In this framework, reaching an allocation that is optimal may require very complex multilateral deals. Therefore, we are interested in identifying classes of valuation functions such that any negotiation conducted by means of deals involving only a single resource at a time is bound to converge to an optimal allocation whenever all agents model their preferences using these functions. In the case of negotiation with monetary side payments amongst selfinterested but myopic agents, the class of modular valuation functions turns out to be such a class. That is, modularity is a sufficient condition for convergence in this framework. We also show that modularity is not a necessary condition. Indeed, there can be no condition on individual valuation functions that would be both necessary and sufficient in this sense. Evaluating conditions formulated with respect to the whole profile of valuation functions used by the agents in the system would be possible in theory, but turns out to be computationally intractable in practice. Our main result shows that the class of modular functions is maximal in the sense that no strictly larger class of valuation functions would still guarantee an optimal outcome of negotiation, even when we permit more general bilateral deals. We also establish similar results in the context of negotiation without side payments.
Instruction Issue Logic in Pipelined Supercomputers
 In COMSOC’06: International Workshop on Computational Social Choice
, 2006
"... In this paper we deal with the problem of optimally placing a set of query operators in an overlay network. Each user is interested in performing a query on streaming data and each query has an associated set of innetwork operators that filter, aggregate and process the data in various ways. Each us ..."
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Cited by 7 (3 self)
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In this paper we deal with the problem of optimally placing a set of query operators in an overlay network. Each user is interested in performing a query on streaming data and each query has an associated set of innetwork operators that filter, aggregate and process the data in various ways. Each user has private information about the operators associated with a query and about the utility from different combinations of operator placements. Each server in the overlay network is able to perform some set of operators, and servers differ in their network and computational characteristics. We model this problem as a Distributed Constraint Optimization Problem (DCOP), and apply the MDPOP algorithm from Petcu et al. [19], executed here by clients associated with users and situated at nodes on the overlay network. MDPOP makes truthtelling an expost Nash equilibrium and determines the socialwelfare maximizing placement of operators to servers. No client can benefit by deviating from the MDPOP algorithm and nodes need only communicate with other nodes that have an interest in placing an operator on the same server. The only central authority required is a bank that can extract payments from users. Preliminary results from simulation show that message size will be a bottleneck in applying MDPOP to operator placement unless structure can be enforced and then exploited. 1
Evaluation of CBR on Live Networks
"... Abstract. A large class of problems in multiagent systems can be solved by distributed constraint optimization (DCOP). Several algorithms have been created to solve these problems, however, no extensive evaluation of current DCOP algorithms on live networks exists in the literature. This paper uses ..."
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Cited by 6 (4 self)
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Abstract. A large class of problems in multiagent systems can be solved by distributed constraint optimization (DCOP). Several algorithms have been created to solve these problems, however, no extensive evaluation of current DCOP algorithms on live networks exists in the literature. This paper uses DCOPolis—a framework for comparing and deploying DCOP software in heterogeneous environments—to contribute an analysis of two stateoftheart DCOP algorithms run in various network environments solving a number of different problem types. Then, we use this empirical validation to evaluate the use of both cyclebased runtime and concurrent constraint checks. 1