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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)
Traditional centralised approaches to security are difficult to apply to large, distributed marketplaces in which software agents operate. Developing a notion of trust that is based on the reputation of agents can provide a softer notion of security that is sufficient for many multi-agent applications. In this paper, we address the issue of(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)
Many problem-solving tasks can be formalized as constraint satisfaction problems (CSPs). In a multi-agent setting, information about constraints and variables may belong to different agents and be kept confidential. Existing algorithms for distributed constraint satisfaction consider mainly the case where access to variables is restricted to certain agents,(More)
We consider constraint satisfaction problems with variables in continuous, numerical domains. Contrary to most existing techniques, which focus on computing one single optimal solution, we address the problem of computing a compact representation of the space of all solutions admitted by the constraints. In particular, we show how globally consistent (also(More)
When searching for configurable products, helping users to state their preferences is a crucial task. It involves helping users to understand the space of feasible configurations to decide on realistic preferences. However, many computer tools do not afford users to adequately focus on fundamental decision objectives, reveal hidden preferences, revise(More)
Constraints are a powerful general paradigm for representing knowledge in intelligent systems. The standard constraint satisfaction paradigm involves variables over a discrete value domain and constraints which restrict the solutions to allowed value combinations. This standard paradigm is inapplicable to problems which are either: (a) mixed, involving both(More)
This paper investigates the problem of reasoning about the kinematic interactions between parts of a mechanism We introduce the concept of Place Vocabularies as a useful symbolic description of the possible interactions We examine the requirements for the representation and introduce a definition of place vocabularies that satisfies them We show how this(More)