Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Dynamic variable ordering for ordered binary decision diagrams
ICCAD '93 Proceedings of the 1993 IEEE/ACM international conference on Computer-aided design
Algebraic decision diagrams and their applications
ICCAD '93 Proceedings of the 1993 IEEE/ACM international conference on Computer-aided design
Dynamic Programming
Max-norm projections for factored MDPs
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
SPUDD: stochastic planning using decision diagrams
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
An Inductive Logic Programming Approach to Statistical Relational Learning
Proceedings of the 2005 conference on An Inductive Logic Programming Approach to Statistical Relational Learning
AND/OR multi-valued decision diagrams (AOMDDs) for graphical models
Journal of Artificial Intelligence Research
Solving #SAT and Bayesian inference with backtracking search
Journal of Artificial Intelligence Research
On valued negation normal form formulas
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Compiling Bayesian networks using variable elimination
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Compiling Bayesian networks by symbolic probability calculation based on zero-suppressed BDDs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Approximate dynamic programming with affine ADDs
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Interactive cost configuration over decision diagrams
Journal of Artificial Intelligence Research
Efficient solutions to factored MDPs with imprecise transition probabilities
Artificial Intelligence
Local structure and determinism in probabilistic databases
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
DetH: approximate hierarchical solution of large Markov decision processes
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
A model-learner pattern for bayesian reasoning
POPL '13 Proceedings of the 40th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Bayesian inference using data flow analysis
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
Semiring labelled decision diagrams, revisited: canonicity and spatial efficiency issues
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We propose an affine extension to ADDs (AADD) capable of compactly representing context-specific, additive, and multiplicative structure. We show that the AADD has worst-case time and space performance within a multiplicative constant of that of ADDs, but that it can be linear in the number of variables in cases where ADDs are exponential in the number of variables. We provide an empirical comparison of tabular, ADD, and AADD representations used in standard Bayes net and MDP inference algorithms and conclude that the AADD performs at least as well as the other two representations, and often yields an exponential performance improvement over both when additive or multiplicative structure can be exploited. These results suggest that the AADD is likely to yield exponential time and space improvements for a variety of probabilistic inference algorithms that currently use tables or ADDs.