Efficient algorithms for combinatorial problems on graphs with bounded, decomposability—a survey
BIT - Ellis Horwood series in artificial intelligence
Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A valuation-based language for expert systems
International Journal of Approximate Reasoning
Modern heuristic techniques for combinatorial problems
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Finding MAPs for belief networks is NP-hard
Artificial Intelligence
On the hardness of approximate reasoning
Artificial Intelligence
Approximating MAPs for belief networks is NP-hard and other theorems
Artificial Intelligence
Boosting combinatorial search through randomization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Towards a characterisation of the behaviour of stochastic local search algorithms for SAT
Artificial Intelligence
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Topological parameters for time-space tradeoff
Artificial Intelligence
A machine program for theorem-proving
Communications of the ACM
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Local search characteristics of incomplete SAT procedures
Artificial Intelligence
Guided Local Search for Solving SAT and Weighted MAX-SAT Problems
Journal of Automated Reasoning
Local Search Algorithms for SAT: An Empirical Evaluation
Journal of Automated Reasoning
The Challenges of Real-Time AI
Computer
Algorithm Selection using Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Approximating MAP using Local Search
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
A Bayesian Approach to Tackling Hard Computational Problems
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Restart Policies with Dependence among Runs: A Dynamic Programming Approach
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
A differential approach to inference in Bayesian networks
Journal of the ACM (JACM)
An adaptive noise mechanism for walkSAT
Eighteenth national conference on Artificial intelligence
A mixture-model for the behaviour of SLS algorithms for SAT
Eighteenth national conference on Artificial intelligence
Using weighted MAX-SAT engines to solve MPE
Eighteenth national conference on Artificial intelligence
Stochastic Greedy Search: Efficiently Computing a Most Probable Explanation in Bayesian Networks
Stochastic Greedy Search: Efficiently Computing a Most Probable Explanation in Bayesian Networks
Efficient bayesian network inference: genetic algorithms, stochastic local search, and abstraction
Efficient bayesian network inference: genetic algorithms, stochastic local search, and abstraction
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
GRASP with path relinking for the weighted MAXSAT problem
Journal of Experimental Algorithmics (JEA)
AND/OR search spaces for graphical models
Artificial Intelligence
A logical approach to efficient Max-SAT solving
Artificial Intelligence
On probabilistic inference by weighted model counting
Artificial Intelligence
Understanding the role of noise in stochastic local search: Analysis and experiments
Artificial Intelligence
Memory intensive branch-and-bound search for graphical models
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Macroscopic models of clique tree growth for Bayesian networks
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Diagnosing faults in electrical power systems of spacecraft and aircraft
IAAI'08 Proceedings of the 20th national conference on Innovative applications of artificial intelligence - Volume 3
Complexity results and approximation strategies for MAP explanations
Journal of Artificial Intelligence Research
New inference rules for Max-SAT
Journal of Artificial Intelligence Research
MINIMAXSAT: an efficient weighted max-SAT solver
Journal of Artificial Intelligence Research
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
HUGIN: a shell for building Bayesian belief universes for expert systems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Domain-independent extensions to GSAT: solving large structured satisfiability problems
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
A dynamic approach to MPE and weighted MAX-SAT
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
Efficient stochastic local search for MPE solving
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
AND/OR branch-and-bound for graphical models
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Compiling Bayesian networks with local structure
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
MaxSolver: An efficient exact algorithm for (weighted) maximum satisfiability
Artificial Intelligence
SATzilla-07: the design and analysis of an algorithm portfolio for SAT
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
A new method for solving hard satisfiability problems
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Hard and easy distributions of SAT problems
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Evidence for invariants in local search
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Tuning local search for satisfiability testing
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
IEEE Transactions on Knowledge and Data Engineering
Encoding CNFs to empower component analysis
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
Understanding the scalability of Bayesian network inference using clique tree growth curves
Artificial Intelligence
Towards software health management with bayesian networks
Proceedings of the FSE/SDP workshop on Future of software engineering research
Software health management with Bayesian networks
Innovations in Systems and Software Engineering
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Portfolio methods support the combination of different algorithms and heuristics, including stochastic local search (SLS) heuristics, and have been identified as a promising approach to solve computationally hard problems. While successful in experiments, theoretical foundations and analytical results for portfolio-based SLS heuristics are less developed. This article aims to improve the understanding of the role of portfolios of heuristics in SLS. We emphasize the problem of computing most probable explanations (MPEs) in Bayesian networks (BNs). Algorithmically, we discuss a portfolio-based SLS algorithm for MPE computation, Stochastic Greedy Search (SGS). SGS supports the integration of different initialization operators (or initialization heuristics) and different search operators (greedy and noisy heuristics), thereby enabling new analytical and experimental results. Analytically, we introduce a novel Markov chain model tailored to portfolio-based SLS algorithms including SGS, thereby enabling us to analytically form expected hitting time results that explain empirical run time results. For a specific BN, we show the benefit of using a homogenous initialization portfolio. To further illustrate the portfolio approach, we consider novel additive search heuristics for handling determinism in the form of zero entries in conditional probability tables in BNs. Our additive approach adds rather than multiplies probabilities when computing the utility of an explanation. We motivate the additive measure by studying the dramatic impact of zero entries in conditional probability tables on the number of zero-probability explanations, which again complicates the search process. We consider the relationship between MAXSAT and MPE, and show that additive utility (or gain) is a generalization, to the probabilistic setting, of MAXSAT utility (or gain) used in the celebrated GSAT and WalkSAT algorithms and their descendants. Utilizing our Markov chain framework, we show that expected hitting time is a rational function--i.e. a ratio of two polynomials--of the probability of applying an additive search operator. Experimentally, we report on synthetically generated BNs as well as BNs from applications, and compare SGS's performance to that of Hugin, which performs BN inference by compilation to and propagation in clique trees. On synthetic networks, SGS speeds up computation by approximately two orders of magnitude compared to Hugin. In application networks, our approach is highly competitive in Bayesian networks with a high degree of determinism. In addition to showing that stochastic local search can be competitive with clique tree clustering, our empirical results provide an improved understanding of the circumstances under which portfolio-based SLS outperforms clique tree clustering and vice versa.