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46
BnBADOPT: An asynchronous branchandbound DCOP algorithm
 In Proceedings of AAMAS
, 2008
"... Abstract. Distributed constraint optimization problems (DCOPs) are a popular way of formulating and solving agentcoordination problems. It is often desirable to solve DCOPs optimally with memorybounded and asynchronous algorithms. We thus introduce BranchandBound ADOPT (BnBADOPT), a memoryboun ..."
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Cited by 61 (21 self)
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Abstract. Distributed constraint optimization problems (DCOPs) are a popular way of formulating and solving agentcoordination problems. It is often desirable to solve DCOPs optimally with memorybounded and asynchronous algorithms. We thus introduce BranchandBound ADOPT (BnBADOPT), a memorybounded asynchronous DCOP algorithm that uses the message passing and communication framework of ADOPT, a well known memorybounded asynchronous DCOP algorithm, but changes the search strategy of ADOPT from bestfirst search to depthfirst branchandbound search. Our experimental results show that BnBADOPT is up to one order of magnitude faster than ADOPT on a variety of large DCOPs and faster than NCBB, a memorybounded synchronous DCOP algorithm, on most of these DCOPs. 1
Reasoning about soft constraints and conditional preferences: Complexity results and approximation techniques
 In Proceedings of IJCAI2003
, 2003
"... Many real life optimization problems contain both hard and soft constraints, as well as qualitative conditional preferences. However, there is no single formalism to specify all three kinds of information. We therefore propose a framework, based on both CPnets and soft constraints, that handles bot ..."
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Cited by 39 (16 self)
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Many real life optimization problems contain both hard and soft constraints, as well as qualitative conditional preferences. However, there is no single formalism to specify all three kinds of information. We therefore propose a framework, based on both CPnets and soft constraints, that handles both hard and soft constraints as well as conditional preferences efficiently and uniformly. We study the complexity of testing the consistency of preference statements, and show how soft constraints can faithfully approximate the semantics of conditional preference statements whilst improving the computational complexity. 1
Using privacy loss to guide decisions in distributed CSP search
 In FLAIRS’04
, 2004
"... In cooperative problem solving, the communication necessary for solution search can also lead to privacy loss on the part of the agents involved. Such loss can be assessed either by directly tallying the number and importance of specific items of information revealed or by tracking reductions in the ..."
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Cited by 19 (15 self)
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In cooperative problem solving, the communication necessary for solution search can also lead to privacy loss on the part of the agents involved. Such loss can be assessed either by directly tallying the number and importance of specific items of information revealed or by tracking reductions in the set of possible values associated with a particular item of information. In both cases information loss can occur either because of direct communication or by inferences that other agents make from one’s communications. The results of these inferences are stored in the “views ” that agents have of other agents. In the present work on distributed constraint solving, such views are organized as extensions of normal CSP representations that model information about possible values in unknown CSPs of other agents. Here we show how this approach can be extended so that agents also maintain views of other agents ’ views of themselves; the latter are called “mirror views”. Mirror views can in turn be used to monitor one’s own privacy loss, and can support strategies designed to reduce the loss of particular kinds of private information. Experiments with a simulated meeting scheduling system show that it is possible to reduce privacy loss with strategies based on mirror views.
Soft Constraint Programming to Analysing Security Protocols
 THEORY AND PRACTICE OF LOGIC PROGRAMMING
, 2004
"... Security protocols stipulate how the remote principals of a computer network should interact in order to obtain specific security goals. The crucial goals of confidentiality and authentication may be achieved in various forms, each of different strength. Using soft (rather than crisp) constraints, w ..."
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Cited by 18 (10 self)
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Security protocols stipulate how the remote principals of a computer network should interact in order to obtain specific security goals. The crucial goals of confidentiality and authentication may be achieved in various forms, each of different strength. Using soft (rather than crisp) constraints, we develop a uniform formal notion for the two goals. They are no longer formalised as mere yes/no properties as in the existing literature, but gain an extra parameter, the security level. For example, different messages can enjoy different levels of confidentiality, or a principal can achieve different levels of authentication with different principals. The goals are formalised within a general framework for protocol analysis that is amenable to mechanisation by model checking. Following the application of the framework to analysing the asymmetric NeedhamSchroeder protocol (Bella and Bistarelli 2001; Bella and Bistarelli 2002), we have recently discovered a new attack on that protocol as a form of retaliation by principals who have been attacked previously. Having commented on that attack, we then demonstrate the framework on a bigger, largely deployed protocol consisting of three phases, Kerberos.
Abstracting Soft Constraints: Framework, Properties, Examples
 Artificial Intelligence
, 2002
"... Soft constraints are very exible and expressive. However, they also are very complex to handle. For this reason, it may be reasonable in several cases to pass to an abstract version of a given soft constraint problem, and then to bring some useful information from the abstract problem to the concret ..."
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Cited by 18 (9 self)
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Soft constraints are very exible and expressive. However, they also are very complex to handle. For this reason, it may be reasonable in several cases to pass to an abstract version of a given soft constraint problem, and then to bring some useful information from the abstract problem to the concrete one. This will hopefully make the search for a solution, or for an optimal solution, of the concrete problem, faster.
Certainty closure: A framework for reliable constraint reasoning with uncertainty
 in Proc. CP’03
, 2003
"... Abstract Constraint problems with incomplete or erroneous data are often simplified to tractable deterministic models, or modified using error correction methods, with the aim of seeking a solution. However, this can lead us to solve the wrong problem because of the approximations made. Such an ou ..."
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Cited by 18 (4 self)
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Abstract Constraint problems with incomplete or erroneous data are often simplified to tractable deterministic models, or modified using error correction methods, with the aim of seeking a solution. However, this can lead us to solve the wrong problem because of the approximations made. Such an outcome is of little help to a user who expects the right problem to be tackled and reliable information returned. The certainty closure framework we present aims to provide the user with reliable insight by: (1) enclosing the uncertainty using what is known for sure about the data, to guarantee that the true problem is contained in the model so described, (2) deriving a closure, a set of possible solutions to the uncertain constraint problem. In this paper we first demonstrate the benefits of reliable constraint reasoning on two different case studies, and then generalise our approaches into a formal framework. 1
Abstracting soft constraints
 PROC. 1999 ERCIM/COMPULOG NET WORKSHOP ON CONSTRAINTS, SPRINGER LNAI 1865
, 2000
"... We propose an abstraction scheme for soft constraint problems and we study its main properties. Processing the abstracted version of a soft constraint problem can help us in many ways: for example, to nd good approximations of the optimal solutions, or also to provide us with information that can m ..."
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Cited by 10 (6 self)
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We propose an abstraction scheme for soft constraint problems and we study its main properties. Processing the abstracted version of a soft constraint problem can help us in many ways: for example, to nd good approximations of the optimal solutions, or also to provide us with information that can make the subsequent search for the best solution easier. The results of this paper show that the proposed scheme is promising; thus they can be used as a stable formal base for any experimental work specific to a particular class of soft constraint problems.
Bucket and minibucket Schemes for M Best Solutions over Graphical Models
"... The paper focuses on finding the m best solutions of a combinatorial optimization problem defined over a graphical model (e.g., the m most probable explanations for a Bayesian network). We describe elimmopt, a new bucket elimination algorithm for solving the mbest task, provide efficient implement ..."
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Cited by 10 (3 self)
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The paper focuses on finding the m best solutions of a combinatorial optimization problem defined over a graphical model (e.g., the m most probable explanations for a Bayesian network). We describe elimmopt, a new bucket elimination algorithm for solving the mbest task, provide efficient implementation of its defining combination and marginalization operators, analyze its worstcase performance, and compare it with that of recent related algorithms. An extension to the minibucket framework, yielding a collection of bounds for each of the mbest solutions is discussed and empirically evaluated. We also formulate the mbest task as a regular reasoning task over general graphical models defined axiomatically, which makes all other inference algorithms applicable. 1
An Abstraction Framework for Soft Constraints, And Its Relationship with Constraint Propagation
 In
, 2000
"... . Soft constraints are very flexible and expressive. However, they also are very complex to handle. For this reason, it may reasonable in several cases to pass to an abstract version of a given soft problem, and then to bring some useful information from the abstract problem to the concrete one. Thi ..."
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Cited by 9 (4 self)
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. Soft constraints are very flexible and expressive. However, they also are very complex to handle. For this reason, it may reasonable in several cases to pass to an abstract version of a given soft problem, and then to bring some useful information from the abstract problem to the concrete one. This will hopefully make the search for a solution, or for an optimal solution, of the concrete problem, faster. In this paper we review the main concepts and properties of our abstraction framework for soft constraints, and we show how it can be used to import constraint propagation algorithms from the abstract scenario to the concrete one. This may be useful when we don't have any (or any e#cient) propagation algorithm in the concrete setting. 1
Hard and soft constraints for reasoning about qualitative conditional preferences
"... Abstract. Many real life optimization problems are defined in terms of both hard and soft constraints, and qualitative conditional preferences. However, there is as yet no single framework for combined reasoning about these three kinds of information. In this paper we study how to exploit classical ..."
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Cited by 8 (2 self)
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Abstract. Many real life optimization problems are defined in terms of both hard and soft constraints, and qualitative conditional preferences. However, there is as yet no single framework for combined reasoning about these three kinds of information. In this paper we study how to exploit classical and soft constraint solvers for handling qualitative preference statements such as those captured by the CPnets model. In particular, we show how hard constraints are sufficient to model the optimal outcomes of a possibly cyclic CPnet, and how soft constraints can faithfully approximate the semantics of acyclic conditional preference statements whilst improving the computational efficiency of reasoning about these statements. 1