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48
Open constraint programming
- ARTIFICIAL INTELLIGENCE 161 (2005) 181–208
, 2005
"... Traditionally, constraint satisfaction problems (CSP) have assumed closed-world scenarios where all domains and constraints are fixed from the beginning. With the Internet, many of the traditional CSP applications in resource allocation, scheduling and planning pose themselves in open-world settings ..."
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Cited by 38 (5 self)
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Traditionally, constraint satisfaction problems (CSP) have assumed closed-world scenarios where all domains and constraints are fixed from the beginning. With the Internet, many of the traditional CSP applications in resource allocation, scheduling and planning pose themselves in open-world settings, where domains and constraints must be discovered from different sources in a network. To model this scenario, we define open constraint satisfaction problems (OCSP) as CSP where domains and constraints are incrementally discovered through a network. We then extend the concept to open constraint optimization (OCOP). OCSP can be solved without complete knowledge of the variable domains, and we give sound and complete algorithms. We show that OCOP require the additional assumption that variable domains and relations are revealed in non-decreasing order of preference. We present a variety of algorithms for solving OCOP in the possibilistic and weighted model. We compare the algorithms through experiments on randomly generated problems. We show that in certain cases, open constraint programming can require significantly less information than traditional methods where gathering information and solving the CSP are separated. This leads to a reduction in network traffic and server load, and improves privacy in distributed problem solving.
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, best-first fashion suit ..."
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Cited by 30 (6 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, best-first 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 non-increasing 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
MB-DPOP: A new memory-bounded 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 25 (6 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 MB-DPOP, a new hybrid algorithm that can operate with bounded memory. In areas of low width, MB-DPOP operates like standard DPOP (linear number of messages). Areas of high width are explored with bounded propagations using the idea of cycle-cuts [Dechter, 2003]. We introduce novel DFS-based mechanisms for determining the cycle-cutset, and for grouping cycle-cut 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 MB-DPOP with ADOPT [Modi et al., 2005], the current state of the art in distributed search with bounded memory. MB-DPOP consistently outperforms ADOPT on 3 problem domains, with respect to 3 metrics, providing speedups of up to 5 orders of magnitude. 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 21 (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 self-interest (as captured by a set-Nash 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.
PC-DPOP: 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 PC-DPOP, a new ..."
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Cited by 20 (4 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 PC-DPOP, a new partial centralization technique, based on the DPOP algorithm of [Petcu and Faltings, 2005]. PC-DPOP 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, PC-DPOP 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
Auctions and Bidding: A Guide for Computer Scientists
"... There is a veritable menagerie of auctions — single-dimensional, multi-dimensional, single-sided, ..."
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Cited by 17 (0 self)
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There is a veritable menagerie of auctions — single-dimensional, multi-dimensional, single-sided,
Privacy-Preserving Multi-agent 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 15 (6 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.
Optimizing streaming applications with self-interested users using M-DPOP
- In COMSOC’06: International Workshop on Computational Social Choice
, 2006
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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 12 (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 self-interested 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.
