Daniel Frost

AIJ Journal 2002 Journal Article

Backjump-based backtracking for constraint satisfaction problems

• Rina Dechter
• Daniel Frost

The performance of backtracking algorithms for solving finite-domain constraint satisfaction problems can be improved substantially by look-back and look-ahead methods. Look-back techniques extract information by analyzing failing search paths that are terminated by dead-ends. Look-ahead techniques use constraint propagation algorithms to avoid such dead-ends altogether. This paper describes a number of look-back variants including backjumping and constraint recording which recognize and avoid some unnecessary explorations of the search space. The last portion of the paper gives an overview of look-ahead methods such as forward checking and dynamic variable ordering, and discusses their combination with backjumping.

AAAI Conference 1997 Conference Paper

Summarizing CSP Hardness with Continuous Probability Distributions

• Daniel Frost

IJCAI Conference 1995 Conference Paper

Look-ahead value ordering for constraint satisfaction problems

• Daniel Frost
• Rina Dechter

Looking ahead during search is often useful when solving constraint satisfaction problems. Previous studies have shown that looking ahead helps by causing dead-ends to occur earlier in the search, and by providing information that is useful for dynamic variable ordering. In this paper, we show that another benefit of looking ahead is a useful domain value ordering heuristic, which we call look-ahead value ordering or LVO. LVO counts the number of times each value of the current variable conflicts with some value of a future variable, and the value with the lowest number of conflicts is chosen first. Our experiments show that look-ahead value ordering can be of substantial benefit, especially on hard constraint satisfaction problems.

AAAI Conference 1994 Conference Paper

Dead-End Driven Learning

• Daniel Frost

The paper evaluates the effectiveness of learning for speeding up the solution of constraint satisfaction problems. It extends previous work (Dechter 1990) by introducing a new and powerful variant of learning and by presenting an extensive empirical study on much larger and more difficult problem instances. Our results show that learning can speed up backjumping when using either a fixed or dynamic variable ordering. However, the improvement with a dynamic variable ordering is not as great, and for some classes of problems learning is helpful only when a limit is placed on the size of new constraints learned.

AAAI Conference 1994 Conference Paper

In Search of the Best Constraint Satisfaction Search

• Daniel Frost

We present the results of an empirical study of several constraint satisfaction search algorithms and heuristics. Using a random problem generator that allows us to create instances with given characteristics, we show how the relative performance of various search methods varies with the number of variables, the tightness of the constraints, and the sparseness of the constraint graph. A version of backjumping using a dynamic variable ordering heuristic is shown to be extremely effective on a wide range of problems. We conducted our experiments with problem instances drawn from the 50% satisfiable range.

ECAI Conference 1992 Conference Paper

Identification of Domain-Specific Terminology by Combining Mutual Information and Lexical Induction

• C. Gierl
• Daniel Frost