This unit discusses constraint satisfaction problems (CSPs), a class of problems that consist of variables with constraints on them, which are represented in a simple, standardized, and structured way, such as a constraint graph. CSPs are prevalent in many different application fields, and thus draw lot of attention from the AI and computing science community. This unit focuses on the definition of CSP, and backtracking and intelligent backtracking search for CSPs. This unit also discusses the CSP problem structure and local search. Finally, distributed CSP is introduced in the required readings.
When you complete this unit, you will be able to
Section 1: Constraint satisfaction problems
Section 2: Backtracking and intelligent backtracking search for CSPs
Section 3: Local search and problem structures of CSPs
Section 4: Distributed CSPs
Browse through some of the papers from Faltings, B., and Yokoo, M. (eds.). (2005). Special issue: Distributed constraint satisfaction. Artificial Intelligence, 161(1-2), 1-250.
Bordeaux, L., Hamadi, Y., and Zhang, L. (2006). Propositional satisfiability and constraint programming: A comparative survey. ACM Computing Surveys. 38(4), Article 12. https://doi.acm.org/10.1145/1177352.1177354
Marriott, K., and Stuckey, P. J. (1998). Programming with Constraints: An Introduction. Cambridge, MA: MIT Press,.
Dechter, R. (2003). Constraint Processing. San Francisco, CA: Morgan Kaufmann.
Related journals: Artificial Intelligence, and Constraints
Related conferences: International Conference on Principles and Practice of Constraint Programming. (See: https://link.springer.com/conference/cp/). Note that the full-text proceedings from 2000 to 2009 are available via AU Library.
Note: The journals and conference papers can be accessed through the AU Library links to Elsevier or Springer
Updated December 16 2021 by FST Course Production Staff