Network-based heuristics for constraint satisfaction problems

@inproceedings{Dechter1988NetworkbasedHF,
  title={Network-based heuristics for constraint satisfaction problems},
  author={Rina Dechter and Judea Pearl},
  year={1988}
}
Many AI tasks can be formulated as Constraint-Satisfaction problems (CSP), i.e., the assignment of values to variables subject to a set of constraints. While some CSPs are hard, those that are easy can often be mapped into sparse networks of constraints which, in the extreme case, are trees. This paper identifies classes of problems that lend themselves to easy solutions, and develops algorithms that solve these problems optimally. The paper then presents a method of generating heuristic advice… 

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References

SHOWING 1-10 OF 29 REFERENCES

The Anatomy of Easy Problems: A Constraint-Satisfaction Formulation

TLDR
A subset of CSPs is identified whose syntactic and semantic properties make them easy to solve, and problems supported by tree-like constraint graphs, and some width-2 graphs are chosen as the target model for the relaxation scheme.

Consistency in Networks of Relations

A Sufficient Condition for Backtrack-Free Search

TLDR
A relationship involving the structure of the constraints is described which characterizes to some degree the extreme case of mimmum backtracking and a concept called "width," which may provide some guidance in the representation of constraint satisfaction problems and the order in which they are searched.

A sufficient condition for backtrack-bounded search

TLDR
A relationship involving the structure of the constraints is described that provides a bound on the backtracking required to advance deeper into the backtrack tree, which leads to upper bounds on the effort required for solution of a class of constraint satisfaction problems.

On the Discovery and Generation of Certain Heuristics

  • J. Pearl
  • Computer Science, Business
    AI Mag.
  • 1983
TLDR
It is demonstrated that these heuristics can be obtained by the process of deleting constraints from the original problem and solving the relaxed problem which ensues, and a scheme for generating such heuristic mechanically is outlined.

Intelligent Backtracking in Plan-Based Deduction

TLDR
It is proven that the algorithm is complete in the following sense: if for a given base a resolution refutation exists, then this refutation is found by the algorithm.

Complexity of finding embeddings in a k -tree

TLDR
This work determines the complexity status of two problems related to finding the smallest number k such that a given graph is a partial k-tree and presents an algorithm with polynomially bounded (but exponential in k) worst case time complexity.