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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 cyclic(More)
We present a parameterized approximation scheme for distributed combinatorial optimization problems based on dynamic programming. The algorithm is a utility propagation method and requires a linear number of messages. For exact computation, the size of the largest message is exponential in the width of the constraint graph. We present a distributed(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 planning, scheduling, distributed control, resource allocation, etc. A major challenge in such systems is coordinating(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)
The reason for using distributed constraint satisfaction algorithms is often to allow agents to find a solution while revealing as little as possible about their variables and constraints. So far, most algorithms for DisCSP do not guarantee privacy of this information. This paper describes some simple techniques that can be used with DisCSP algorithms such(More)
In the context of an intelligent habitat assisting an occupant with Alzheimer's disease, the goal of plan recognition is to predict the patient's behavior in order to identify the various ways of supporting him in carrying out his daily activities. However, this situation raises the following dilemma: the observation of a new action, different from the(More)