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We present in this paper a new, complete method for distributed constraint optimization, based on dynamic programming. It is a utility propagation method, inspired by the sum-product algorithm, which 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(More)
This paper considers the problem of performing decentralised coordination of low-power embedded devices (as is required within many environmental sensing and surveillance applications). Specifically , we address the generic problem of maximising social welfare within a group of interacting agents. We propose a novel representation of the problem, as a(More)
We model social choice problems in which self interested agents with private utility functions have to agree on values for a set of variables subject to side constraints. The goal is to implement the efficient solution, maximizing the total utility across all agents. Existing techniques for this problem fall into two groups. Distributed constraint(More)
Multi Agent Systems (MAS) have recently attracted a lot of interest because of their ability to model many real life scenarios where information and control are distributed among a set of different agents. Practical applications include resource allocation , distributed control, scheduling, planning, etc. A major challenge in such systems is coordinating(More)
Distributed constraint optimization (DCOP) provides a framework for coordinated decision making by a team of agents. Often, during the decision making, capacity constraints on agents' resource consumption must be taken into account. To address such scenarios, an extension of DCOP-Resource Constrained DCOP-has been proposed. However, certain type of(More)
In distributed constraint optimization problems, dynamic programming methods have been recently proposed (e.g. DPOP). In dynamic programming many valuations are grouped together in fewer messages, which produce much less networking overhead than search. Nevertheless, these messages are exponential in size. The basic DPOP always communicates all possible(More)
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(More)
In this paper we develop a localized value-ordering heuristic for distributed resource allocation problems. We show how this value ordering heuristics can be used to achieve desirable properties (increased effectiveness, or better allocations). The specific distributed resource allocation problem that we consider is sensor allocation in sensor networks, and(More)