Open Constraint Optimization

Faltings, Boi; Macho-Gonzalez, Santiago

doi:10.1007/978-3-540-45193-8_21

Cited by 14 publications

(7 citation statements)

References 6 publications

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“…As a second example, in dynamic CSPs [2] variables, domains, and constraints may change over time. However, the incompleteness considered in [6,5] is on domain values as well as on their preferences. Working under this assumption means that the agent that provides new values/costs for a variable knows all possible costs, since they are capable of providing the best value first.…”

Section: Introductionmentioning

confidence: 99%

Elicitation Strategies for Fuzzy Constraint Problems with Missing Preferences: Algorithms and Experimental Studies

Gelain

Pini

Rossi

et al. 2008

Lecture Notes in Computer Science

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Abstract. Fuzzy constraints are a popular approach to handle preferences and over-constrained problems in scenarios where one needs to be cautious, such as in medical or space applications. We consider here fuzzy constraint problems where some of the preferences may be missing. This models, for example, settings where agents are distributed and have privacy issues, or where there is an ongoing preference elicitation process. In this setting, we study how to find a solution which is optimal irrespective of the missing preferences. In the process of finding such a solution, we may elicit preferences from the user if necessary. However, our goal is to ask the user as little as possible. We define a combined solving and preference elicitation scheme with a large number of different instantiations, each corresponding to a concrete algorithm which we compare experimentally. We compute both the number of elicited preferences and the "user effort", which may be larger, as it contains all the preference values the user has to compute to be able to respond to the elicitation requests. While the number of elicited preferences is important when the concern is to communicate as little information as possible, the user effort measures also the hidden work the user has to do to be able to communicate the elicited preferences. Our experimental results show that some of our algorithms are very good at finding a necessarily optimal solution while asking the user for only a very small fraction of the missing preferences. The user effort is also very small for the best algorithms. Finally, we test these algorithms on hard constraint problems with possibly missing constraints, where the aim is to find feasible solutions irrespective of the missing constraints.

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Section: Introductionmentioning

confidence: 99%

Elicitation Strategies for Fuzzy Constraint Problems with Missing Preferences: Algorithms and Experimental Studies

Gelain

Pini

Rossi

et al. 2008

Lecture Notes in Computer Science

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“…The motivation for this work is related to part of that for Open Constraints [8][9][10]17], and Interactive CSPs [5,18], with a major difference being our assumption of there being cost and probabilistic information available ( [9] considers costs in optimisation problems, but in a rather different way). Although these kinds of methods could be used for our problem, not taking costs and probabilities into account will, unsurprisingly, tend to generate solutions with poor expected cost, as illustrated by the example and our experimental results.…”

Section: Related Workmentioning

confidence: 99%

“…Constraints and Interactive CSPs [5,[8][9][10]18], there are situations where it can be advantageous and natural to interleave the elicitation and the solving. We may not need all the complete constraints to be available in order for us to find a solution.…”

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confidence: 99%

Interleaving solving and elicitation of constraint satisfaction problems based on expected cost

2010

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We consider Constraint Satisfaction Problems in which constraints can be initially incomplete, where it is unknown whether certain tuples satisfy the constraint or not. We assume that we can determine the satisfaction of such an unknown tuple, i.e., find out whether this tuple is in the constraint or not, but doing so incurs a known cost, which may vary between tuples. We also assume that we know the probability of an unknown tuple satisfying a constraint. We define algorithms for this problem, based on backtracking search. Specifically, we consider a simple iterative algorithm based on a cost limit on the unknowns that may be determined, and a more complex algorithm that delays determining an unknown in order to estimate better whether doing so is worthwhile. We show experimentally that the more sophisticated algorithms can greatly reduce the average cost

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“…L is algorithm independent. So we have time f or cycle k = L + computation time in cycle k (2) In order to measure the computational cost in a cycle, we make use of a recent metric -concurrent constraint checks (ccc) [9]. A constraint check is the act of evaluating a constraint in the problem by comparing the value of one variable to another variable in the problem.…”

Section: Cycle-based Runtimementioning

confidence: 99%

“…The Distributed Constraint Optimization Problem (DCOP) is a general framework for distributed problem solving that has a wide range of applications in Multiagent Systems and has generated significant interest from researchers [6,11,5,4,2,12,8,10]. A DCOP Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionmentioning

confidence: 99%

Impact of problem centralization in distributed constraint optimization algorithms

Davin

Modi

2005

Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems

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Recent progress in Distributed Constraint Optimization Problems (DCOP) has led to a range of algorithms now available which differ in their amount of problem centralization. Problem centralization can have a significant impact on the amount of computation required by an agent but unfortunately the dominant evaluation metric of "number of cycles" fails to account for this cost. We analyze the relative performance of two recent algorithms for DCOP: OptAPO, which performs partial centralization, and Adopt, which maintains distribution of the DCOP. Previous comparison of Adopt and OptAPO has found that OptAPO requires fewer cycles than Adopt. We extend the cycles metric to define "Cycle-Based Runtime (CBR)" to account for both the amount of computation required in each cycle and the communication latency between cycles. Using the CBR metric, we show that Adopt outperforms OptAPO under a range of communication latencies. We also ask: What level of centralization is most suitable for a given communication latency? We use CBR to create performance curves for three algorithms that vary in degree of centralization, namely Adopt, OptAPO, and centralized Branch and Bound search.

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Open Constraint Optimization

Cited by 14 publications

References 6 publications

Elicitation Strategies for Fuzzy Constraint Problems with Missing Preferences: Algorithms and Experimental Studies

Elicitation Strategies for Fuzzy Constraint Problems with Missing Preferences: Algorithms and Experimental Studies

Interleaving solving and elicitation of constraint satisfaction problems based on expected cost

Impact of problem centralization in distributed constraint optimization algorithms

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