Asynchronous Forward-Bounding for Distributed Constraints Optimization

  • Authors:

  • Amir Gershman;Amnon Meisels;Roie Zivan

  • Affiliations:

  • Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, 84-105, Israel. email: {amirger,am,zivanr}@cs.bgu.ac.il;Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, 84-105, Israel. email: {amirger,am,zivanr}@cs.bgu.ac.il;Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, 84-105, Israel. email: {amirger,am,zivanr}@cs.bgu.ac.il

  • Venue:
  • Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
  • Year:
  • 2006

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Abstract

A new search algorithm for solving distributed constraint optimization problems (DisCOPs) is presented. Agents assign variables sequentially and propagate their assignments asynchronously. The asynchronous forward-bounding algorithm (AFB) is a distributed optimization search algorithm that keeps one consistent partial assignment at all times. Forward bounding propagates the bounds on the cost of solutions by sending copies of the partial assignment to all unassigned agents concurrently. The algorithm is described in detail and its correctness proven. Experimental evaluation of AFB on random Max-DisCSPs reveals a phase transition as the tightness of the problem increases. This effect is analogous to the phase transition of Max-CSP when local consistency maintenance is applied [3]. AFB outperforms Synchronous Branch & Bound (SBB) as well as the asynchronous state-of-the-art ADOPT algorithm, for the harder problem instances. Both asynchronous algorithms outperform SBB by a large factor.