Symbolic Boolean manipulation with ordered binary-decision diagrams
ACM Computing Surveys (CSUR)
Faster SAT and smaller BDDs via common function structure
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Knowledge Engineering for Bayesian Networks: How Common Are Noisy-MAX Distributions in Practice?
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
On compiling system models for faster and more scalable diagnosis
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Model-based diagnosis using structured system descriptions
Journal of Artificial Intelligence Research
Compiling Bayesian networks with local structure
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Using DPLL for efficient OBDD construction
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
Model-Based diagnosis through OBDD compilation: a complexity analysis
Reasoning, Action and Interaction in AI Theories and Systems
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Model-Based Reasoning requires as input a formal model of the system often expressed as a propositional logic theory. Exploiting the presence of structure in such a theory is fundamental in order to have a compact representation of the model and, more important, to speed-up the reasoning task. In this paper we introduce the notion of causal independence (derived from the Bayesian Networks formalism) in order to allow the modeling of an important class of local relations among system variables. In particular we focus our analysis on MAX families, where the value of a common effect is determined as the maximum among the independent contributions of a set of causing variables. We show formal and experimental results on the positive effects of causal independence on the size of the compilation of the system model in terms of an Ordered Binary Decision Diagram and connect them with the computational efficiency of Model-Based Diagnosis. Such benefits hold also when we relax the notion of causal independence in order to cover a broader class of systems which includes combinatorial digital circuits.