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U-GDL: A decentralised algorithm for DCOPs with Uncertainty

U-GDL: A decentralised algorithm for DCOPs with Uncertainty
U-GDL: A decentralised algorithm for DCOPs with Uncertainty
In this paper, we introduce DCOPs with uncertainty (U-DCOPs), a novel generalisation of the canonical DCOP framework where the outcomes of local functions are represented by random variables, and the global objective is to maximise the expectation of an arbitrary utility function (that represents the agents' risk-profile) applied over the sum of these local functions. We then develop U-GDL, a novel decentralised algorithm derived from Generalised Distributive Law (GDL) that optimally solves U-DCOPs. A key property of U-GDL that we show is necessary for optimality is that it keeps track of multiple non-dominated alternatives, and only discards those that are dominated (i.e. local partial solutions that can never turn into an expected global maximum regardless of the realisation of the random variables). As a direct consequence, we show that applying a standard DCOP algorithm to U-DCOP can result in arbitrarily poor solutions. We empirically evaluate U-GDL to determine its computational overhead and bandwidth requirements compared to a standard DCOP algorithm.
Stranders, Ruben
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Delle Fave, Francesco Maria
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Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Stranders, Ruben
cca79d07-0668-4231-a80f-5fae6617644c
Delle Fave, Francesco Maria
1a71a79a-fb96-4bfc-9158-36f8dcb5d96f
Rogers, Alex
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Stranders, Ruben, Delle Fave, Francesco Maria, Rogers, Alex and Jennings, Nick (2011) U-GDL: A decentralised algorithm for DCOPs with Uncertainty (In Press)

Record type: Monograph (Project Report)

Abstract

In this paper, we introduce DCOPs with uncertainty (U-DCOPs), a novel generalisation of the canonical DCOP framework where the outcomes of local functions are represented by random variables, and the global objective is to maximise the expectation of an arbitrary utility function (that represents the agents' risk-profile) applied over the sum of these local functions. We then develop U-GDL, a novel decentralised algorithm derived from Generalised Distributive Law (GDL) that optimally solves U-DCOPs. A key property of U-GDL that we show is necessary for optimality is that it keeps track of multiple non-dominated alternatives, and only discards those that are dominated (i.e. local partial solutions that can never turn into an expected global maximum regardless of the realisation of the random variables). As a direct consequence, we show that applying a standard DCOP algorithm to U-DCOP can result in arbitrarily poor solutions. We empirically evaluate U-GDL to determine its computational overhead and bandwidth requirements compared to a standard DCOP algorithm.

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Accepted/In Press date: 3 May 2011
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 273037
URI: http://eprints.soton.ac.uk/id/eprint/273037
PURE UUID: fb523547-821a-4adb-a399-e6a2c21c31dd

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Date deposited: 30 Nov 2011 13:22
Last modified: 14 Mar 2024 10:17

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Contributors

Author: Ruben Stranders
Author: Francesco Maria Delle Fave
Author: Alex Rogers
Author: Nick Jennings

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