Communications of the ACM
Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Information Processing Letters
Learning regular sets from queries and counterexamples
Information and Computation
The minimum consistent DFA problem cannot be approximated within and polynomial
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
Random DFA's can be approximately learned from sparse uniform examples
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Symbolic Boolean manipulation with ordered binary-decision diagrams
ACM Computing Surveys (CSUR)
On the hardness of approximating minimization problems
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Reduction of OBDDs in linear time
Information Processing Letters
Cryptographic limitations on learning Boolean formulae and finite automata
Journal of the ACM (JACM)
Inference of finite automata using homing sequences
Information and Computation
An introduction to computational learning theory
An introduction to computational learning theory
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
Verification of Synchronous Sequential Machines Based on Symbolic Execution
Proceedings of the International Workshop on Automatic Verification Methods for Finite State Systems
Learning Ordered Binary Decision Diagrams
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
On the influence of the variable ordering for algorithmic learning using OBDDs
Information and Computation
An efficient query learning algorithm for ordered binary decision diagrams
Information and Computation
On the influence of the variable ordering for algorithmic learning using OBDDs
Information and Computation
An efficient query learning algorithm for ordered binary decision diagrams
Information and Computation
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In this paper, we describe how a basic strategy from computational learning theory can be used to attack a class of NP‐hard combinatorial optimization problems. It turns out that the learning strategy can be used as an iterative booster: given a solution to the combinatorial problem, we will start an efficient simulation of a learning algorithm which has a “good chance” to output an improved solution. This boosting technique is a new and surprisingly simple application of an existing learning strategy. It yields a novel heuristic approach to attack NP‐hard optimization problems. It does not apply to each combinatorial problem, but we are able to exactly formalize some sufficient conditions. The new technique applies, for instance, to the problems of minimizing a deterministic finite automaton relative to a given domain, the analogous problem for ordered binary decision diagrams, and to graph coloring.