Publication

A Scalable Method for Multiagent Constraint Optimization

Boi Faltings
2005
Reports

Abstract

We present in this paper a new complete method for distributed constraint optimization. This is a utility-propagation method, inspired by the sum-product algorithm (Kschischang et al 2001). The original algorithm requires fixed message sizes, linear memory and linear time in the size of the problem. However, it is correct only for tree-shaped constraint networks. In this paper, we show how to extend that algorithm to arbitrary topologies using a pseudotree arrangement of the problem graph. We compare our algorithm with "standard" backtracking algorithms, and present experimental results. For some problem types we report orders of magnitude less messages, and even the ability to deal with arbitrary large problems. Our algorithm is formulated for optimization problems, but can be easily applied to satisfaction problems as well.

Official source

https://infoscience.epfl.ch/entities/publication/7f87e38f-63f6-4991-a8e7-20778243fc9f

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Boi Faltings
2005
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/7f87e38f-63f6-4991-a8e7-20778243fc9f

About this result

Related concepts (32)

Constraint satisfaction problem

Constraint satisfaction problems (CSPs) are mathematical questions defined as a set of objects whose state must satisfy a number of constraints or limitations. CSPs represent the entities in a problem as a homogeneous collection of finite constraints over variables, which is solved by constraint satisfaction methods. CSPs are the subject of research in both artificial intelligence and operations research, since the regularity in their formulation provides a common basis to analyze and solve problems of many seemingly unrelated families.

Optimization problem

In mathematics, computer science and economics, an optimization problem is the problem of finding the best solution from all feasible solutions. Optimization problems can be divided into two categories, depending on whether the variables are continuous or discrete: An optimization problem with discrete variables is known as a discrete optimization, in which an object such as an integer, permutation or graph must be found from a countable set.

Constrained optimization

In mathematical optimization, constrained optimization (in some contexts called constraint optimization) is the process of optimizing an objective function with respect to some variables in the presence of constraints on those variables. The objective function is either a cost function or energy function, which is to be minimized, or a reward function or utility function, which is to be maximized.

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