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  • Jinbo Huang
  • 2005
After establishing the unsatisfiability of a SAT instance encoding a typical design task, there is a practical need to identify its minimal unsatisfiable subsets, which pinpoint the reasons for the infeasibility of the design. Due to the potentially expensive computation, existing tools for the extraction of unsatisfiable subformulas do not guarantee the(More)
Fundamentally different from DPLL, a new approach to SAT has recently emerged that abandons search and enlists BDDs to symbolically represent clauses of the CNF. These BDDs are conjoined according to a schedule where some variables may be eliminated by quantification at each step to reduce the size of the intermediate BDDs. SAT solving then reduces to(More)
COMPLAN is a conformant probabilistic planner that finds a plan with maximum probability of success for a given horizon. The core of the planner is a a depth-first branch-and-bound search in the plan space. For each potential search node, an upper bound is computed on the success probability of the best plans under the node, and the node is pruned if this(More)
While the efficiency and scalability of modern SAT technology offers an intriguing alternative approach to constraint solving via translation to SAT, previous work has mostly focused on the translation of specific types of constraints, such as pseudo Boolean constraints, finite integer linear constraints, and constraints given as explicit listings of(More)
This paper is concerned with a class of algorithms that perform exhaustive search on propositional knowledge bases. We show that each of these algorithms defines and generates a propositional language. Specifically, we show that the trace of a search can be interpreted as a combinational circuit, and a search algorithm then defines a propositional language(More)
The MAP (maximum a posteriori hypothesis) problem in Bayesian networks is to find the most likely states of a set of variables given partial evidence on the complement of that set. Standard structure-based inference methods for finding exact solutions to MAP, such as variable elimination and join-tree algorithms, have complexities that are exponential in(More)