Advertisement

Treating Some Constraints as Hard Speeds up the ESG Local Search Algorithm

  • Y. kilani
  • A. MohdZin
Conference paper

Abstract

Local search (LS) methods for solving constraint satisfaction problems (CSP) such as GSAT, WalkSAT and DLM starts the search for a solution from a random assignment. LS then examines the neighbours of this assignment, using the penalty function to determine a better neighbour valuations to move to. It repeats this process until it finds a solution that satisfies all constraints. ICM considers some of the constraints as hard constraints that are always satisfied. In this way, the constraints reduce the possible neighbours in each move and hence the overall search space. We choose the hard constraints in such away that the space of valuations that satisfies these constraints is connected in order to guarantee that a local search can reach any solution from any valuation in this space. In this paper, we incorporate ICM into one of the most recent local search algorithm, ESG, and we show the improvement of the new algorithm.

Keywords

Local Search Constraint Satisfaction Problem Local Search Algorithm Hard Constraint Good Neighbour 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Makworth, A. (1977) Consistency in networks of relations. AI 8(1): 99–118.Google Scholar
  2. [2]
    Fang, H., Kilani, Y., Lee, J., Stucky., P. (2002) Reducing search space in local search for constraint satisfaction. In AAAI, AAAI press, pp. 200–207.Google Scholar
  3. [3]
    Selman, B., Levesque, H., Mitchell, D. (1992). A new method for solving hard satisfiability problems. AAAI, AAAI press, pp. 440–446.Google Scholar
  4. [4]
    Selman, B., Kauts, H. (1993) Domain-independent extensions to GSAT: Solving large structured satisfiability problems. IJCAI, pp. 290–295.Google Scholar
  5. [5]
    Wu, Z., Wah, B. (1999) Trap escaping strategies in discrete lagrangian methods for solving hard satisfiability problems. AAAI, pp. 673–678.Google Scholar
  6. [6]
    Wu, Z., Wah, B. (2000) An efficient global-search strategy in discrete lagrangian methods for solving hard satisfiability problems. AAAI, pp. 310–315.Google Scholar
  7. [7]
    Minton, S., Johnston, M., Philips, A., Laird, P. (1992). Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems. AI 58, pp. 161–205.MathSciNetGoogle Scholar
  8. [8]
    Wu, Z., Wah, B. (2000). An efficient global-search strategy in discrete lagrangian methods for solving hard satisfiability problems. AAAI, AAAI press, pp. 310–315.Google Scholar
  9. [9]
    Selman, B., Kauts, H., Cohen, B. (1994) Noise strategies for improving local search. AAAI, AAAI press, pp. 337–343.Google Scholar
  10. [10]
    Schuurmans, D., Southey, F. (2000). Local search characteristics of incomplete sat procedures. AAAI, AAAI press, pp. 297–302.Google Scholar

Copyright information

© Springer-Verlag/Wien 2005

Authors and Affiliations

  • Y. kilani
    • 1
  • A. MohdZin
    • 2
  1. 1.Prince Hussein bin Abdullah Information Technology CollegeAl Al-bayt UniversityJordan
  2. 2.Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaMalaysia

Personalised recommendations