Artificial Intelligence
Symbolic Boolean manipulation with ordered binary-decision diagrams
ACM Computing Surveys (CSUR)
The complexity of logic-based abduction
Journal of the ACM (JACM)
Improving the Variable Ordering of OBDDs Is NP-Complete
IEEE Transactions on Computers
Decomposable negation normal form
Journal of the ACM (JACM)
The nonapproximability of OBDD minimization
Information and Computation
Preprocessing of intractable problems
Information and Computation
Model-based diagnosis using structured system descriptions
Journal of Artificial Intelligence Research
Automated benchmark model generators for model-based diagnostic inference
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Knowledge Compilation Using Interval Automata and Applications to Planning
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
A benchmark diagnostic model generation system
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on model-based diagnostics
| Hi-index | 0.00 |
Compilation is an important approach to a range of inference problems, since it enables linear-time inference in the size S of the compiled representation. However, the main drawback is that S can be exponentially larger than the size of the original function. To address this issue, we propose an incremental, approximate compilation technique that guarantees a sound and space-bounded compilation for weighted boolean functions, at the expense of query completeness. In particular, our approach selectively compiles all solutions exceeding a particular threshold, given a range of weighting functions, without having to perform inference over the full solution-space. We describe incremental, approximate algorithms for the prime implicant and DNNF compilation languages, and provide empirical evidence that these algorithms enable space reductions of several orders-of-magnitude over the full compilation, while losing relatively little query completeness.