Arrow Research search

Author name cluster

Xinguang Chen

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
1 author row

Possible papers

2

AIJ Journal 2002 Journal Article

Binary vs. non-binary constraints☆☆This paper includes results that first appeared in [1,4,23]. This research has been supported in part by the Canadian Government through their NSERC and IRIS programs, and by the EPSRC Advanced Research Fellowship program.

  • Fahiem Bacchus
  • Xinguang Chen
  • Peter van Beek
  • Toby Walsh

There are two well known transformations from non-binary constraints to binary constraints applicable to constraint satisfaction problems (CSPs) with finite domains: the dual transformation and the hidden (variable) transformation. We perform a detailed formal comparison of these two transformations. Our comparison focuses on two backtracking algorithms that maintain a local consistency property at each node in their search tree: the forward checking and maintaining arc consistency algorithms. We first compare local consistency techniques such as arc consistency in terms of their inferential power when they are applied to the original (non-binary) formulation and to each of its binary transformations. For example, we prove that enforcing arc consistency on the original formulation is equivalent to enforcing it on the hidden transformation. We then extend these results to the two backtracking algorithms. We are able to give either a theoretical bound on how much one formulation is better than another, or examples that show such a bound does not exist. For example, we prove that the performance of the forward checking algorithm applied to the hidden transformation of a problem is within a polynomial bound of the performance of the same algorithm applied to the dual transformation of the problem. Our results can be used to help decide if applying one of these transformations to all (or part) of a constraint satisfaction model would be beneficial.

AAAI Conference 1999 Conference Paper

CPlan: A Constraint Programming Approach to Planning

  • Peter van Beek
  • Xinguang Chen
  • University of Alberta

Constraint programming, a methodology for solving difficult combinatorial problems by representing them as constraint satisfaction problems, has shown that a general purpose search algorithm based on constraint propagation combined with an emphasis on modeling can solve large, practical scheduling problems. Given the success of constraint programming on scheduling problems and the similarity of scheduling to planning, the question arises, would a constraint programming approach work as well in planning? In this paper, we present evidencethat a constraint programming approach to planning does indeed work well and has the advantage in terms of time and space efficiency over the current state-of-the-art planners.

v2026.09.13