Extending dynamic backtracking for distributed constraint satisfaction problems
A recent constructive search technique called dynamic backtracking (DB) achieves a systematic and complete search while allowing significant movement in the search space. The algorithm constructs tuples of inconsistent variable assignments called nogoods. An important issue is managing the number of nogoods constructed and remembered (cached) during the search. The nogood caching scheme for DB limits the size of the cache as the search proceeds through the 0space. Recently a new constructive search algorithm for the distributed constraint satisfaction problem (DCSP) was described called asynchronous backtracking (AB). In this method, agents construct nogoods and convey them to other agents to effect the backtrack search. A obvious question to ask if whether the nogood caching scheme employed by DB can be extended for the DCSP In this paper, we briefly analyse the existing DB caching scheme from this perspective and suggest two new improved caching algorithms. Finally we provide some preliminary experimental evidence that our new caching algorithms outperform dynamic backtrackinq in the multiaqent context.
Keywordsmultiagent systems cooperative problem solving intelligent backtracking distributed constraint satisfaction
Unable to display preview. Download preview PDF.
- Baker, A.B. 1995. Intelligent Backtracking on Constraint Satisfaction Problems: Experimental and Theoretical Results. Ph.D. thesis, Dept. of Computer and Information Systems, Univ. of Oregon.Google Scholar
- Bayardo, R.J. & Miranker, D.P. 1996. A Complexity Analysis of Space-Bounded Learning Algorithms for the Constraint Satisfaction Problem. In proc. AAAI-96: 13th National Conf. on Artificial Intelligence, Portland, Oregon, 298–304.Google Scholar
- Dechter, R. 1992. Constraint Networks. In Encyclopedia of Artificial Intelligence, 2nd ed., 276–285. Wiley.Google Scholar
- Gent, I.P.; MacIntyre, E.; Prosser, P & Walsh, T. 1996. The Constrainedness of Search. In proc. AAAI-96: 13th National Conf. on Artificial Intelligence, Portland, Oregon.Google Scholar
- Ginsberg, M. L. 1993. Dynamic Backtracking. Journal of A.I Research 1, Morgan-Kaufmann, 25–46.Google Scholar
- Ginsberg, M. L. & McAllester, D. 1994. GSAT and Dynamic Backtracking, In proc. 2nd Workshop on Principles and Practice of Constraint Programming, Orcas Island, WA.Google Scholar
- Havens, W. S. 1997. NoGood Caching for MultiAgent Backtrack Search. proc. AAAI-97 Constraints and Agents Workshop, American Assn. for Artifical Intelligence national conf., Providence, Rhode Island, 1997 July 26.Google Scholar
- Yokoo, M.; Ishida, T.; Durfee, E. H. & Kuwabara, K. 1992. Distributed Constraint Satisfaction for Formalizing Distributed Problem Solving, In proc. 12th IEEE Int. Conf. of Distributed Computing Systems, 614–621.Google Scholar
- Yokoo, M. 1993. Dynamic Variable/Value Ordering Heuristics for Solving Large-Scale Distributed Constraint Satisfaction Problems. In proc. 12th Int. Workshop on Distributed Artificial IntelligenceGoogle Scholar