Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Topological parameters for time-space tradeoff
Artificial Intelligence
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Stochastic Boolean Satisfiability
Journal of Automated Reasoning
A Qualitative Linear Utility Theory for Spohn's Theory of Epistemic Beliefs
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Beyond NP: Arc-Consistency for Quantified Constraints
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Hybrid backtracking bounded by tree-decomposition of constraint networks
Artificial Intelligence
Complexity results and approximation strategies for MAP explanations
Journal of Artificial Intelligence Research
AND/OR branch-and-bound for graphical models
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
SAT'05 Proceedings of the 8th international conference on Theory and Applications of Satisfiability Testing
Complexity results and algorithms for possibilistic influence diagrams
Artificial Intelligence
An algebraic graphical model for decision with uncertainties, feasibilities, and utilities
Journal of Artificial Intelligence Research
| Hi-index | 0.00 |
In the last decades, the Satisfiability and Constraint Satisfaction Problem frameworks were extended to integrate aspects such as uncertainties, partial observabilities, or uncontrollabilities. The resulting formalisms, including Quantified Boolean Formulas (QBF), Quantified CSP (QCSP), Stochastic SAT (SSAT), or Stochastic CSP (SCSP), still rely on networks of local functions defining specific graphical models, but they involve queries defined by sequences of distinct elimination operators (∃ and ∀ for QBF and QCSP, max and + for SSAT and SCSP) preventing variables from being considered in an arbitrary order when the problem is solved (be it by tree search or by variable elimination). In this paper, we show that it is possible to take advantage of the actual structure of such multi-operator queries to bring to light new ordering freedoms. This leads to an improved constrained induced-width and doing so to possible exponential gains in complexity. This analysis is performed in a generic semiring-based algebraic framework that makes it applicable to various formalisms. It is related with the quantifier tree approach recently proposed for QBF but it is much more general and gives theoretical bases to observed experimental gains.