Understanding the role of noise in stochastic local search: Analysis and experiments

[1]  Alex Fukunaga,et al.  Robust local search for spacecraft operations using adaptive noise , 2004 .

[2]  Joe Suzuki.  A Markov Chain Analysis on A Genetic Algorithm , 1993, ICGA.

[3]  David J. Spiegelhalter,et al.  Local computations with probabilities on graphical structures and their application to expert systems , 1990 .

[4]  Bart Selman,et al.  Boosting Combinatorial Search Through Randomization , 1998, AAAI/IAAI.

[5]  Andrew P. Sage,et al.  Uncertainty in Artificial Intelligence , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  Erick Cantú-Paz,et al.  Markov chain models of parallel genetic algorithms , 2000, IEEE Trans. Evol. Comput..

[7]  Janet A. Sniezek,et al.  CoRAVEN: modeling and design of a multimedia intelligent infrastructure for collaborative intelligence analysis , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[8]  Ivan Serina,et al.  An Empirical Analysis of Some Heuristic Features for Local Search in LPG , 2004, ICAPS.

[9]  Steen Andreassen,et al.  MUNIN - A Causal Probabilistic Network for Interpretation of Electromyographic Findings , 1987, IJCAI.

[10]  Hector J. Levesque,et al.  A New Method for Solving Hard Satisfiability Problems , 1992, AAAI.

[11]  M. Omizo,et al.  Modeling , 1983, Encyclopedic Dictionary of Archaeology.

[12]  R. Dechter,et al.  Stochastic Local Search for Bayesian Networks , 1999 .

[13]  Max Henrion,et al.  Propagating uncertainty in bayesian networks by probabilistic logic sampling , 1986, UAI.

[14]  Adnan Darwiche,et al.  Approximating MAP using Local Search , 2001, UAI.

[15]  Ole J. Mengshoel.  Macroscopic Models of Clique Tree Growth for Bayesian Networks , 2007, AAAI.

[16]  Jun Gu,et al.  Algorithms for the satisfiability (SAT) problem: A survey , 1996, Satisfiability Problem: Theory and Applications.

[17]  Thomas Stützle,et al.  Efficient Stochastic Local Search for MPE Solving , 2005, IJCAI.

[18]  Yair Weiss,et al.  Finding the M Most Probable Configurations in Arbitrary Graphical Models , 2003, NIPS.

[19]  Henry Kautz,et al.  Hardness-Aware Restart Policies , 2003 .

[20]  Bart Selman,et al.  Evidence for Invariants in Local Search , 1997, AAAI/IAAI.

[21]  Dale Schuurmans,et al.  Local search characteristics of incomplete SAT procedures , 2000, Artif. Intell..

[22]  David E. Goldberg,et al.  Finite Markov Chain Analysis of Genetic Algorithms , 1987, ICGA.

[23]  Ashraf M. Abdelbar,et al.  Approximating MAPs for Belief Networks is NP-Hard and Other Theorems , 1998, Artif. Intell..

[24]  John J. Grefenstette,et al.  Genetic algorithms and their applications , 1987 .

[25]  Holger H. Hoos,et al.  An adaptive noise mechanism for walkSAT , 2002, AAAI/IAAI.

[26]  Bart Selman,et al.  Noise Strategies for Improving Local Search , 1994, AAAI.

[27]  Bart Selman,et al.  Domain-Independent Extensions to GSAT : Solving Large StructuredSatis ability , 1993 .

[28]  Rina Dechter,et al.  Stochastic local search for Bayesian network , 1999, AISTATS.

[29]  David E. Goldberg,et al.  The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations , 1999, Evolutionary Computation.

[30]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[31]  Ole J. Mengshoel,et al.  Designing Resource-Bounded Reasoners using Bayesian Networks: System Health Monitoring and Diagnosis , 2007 .

[32]  Makoto Yokoo,et al.  Why Adding More Constraints Makes a Problem Easier for Hill-climbing Algorithms: Analyzing Landscapes of CSPs , 1997, CP.

[33]  Thomas Stützle,et al.  Local Search Algorithms for SAT: An Empirical Evaluation , 2000, Journal of Automated Reasoning.

[34]  Thomas Stützle,et al.  Stochastic Local Search: Foundations & Applications , 2004 .

[35]  David E. Goldberg,et al.  The gambler''s ruin problem , 1997 .

[36]  Dan Roth,et al.  On the Hardness of Approximate Reasoning , 1993, IJCAI.

[37]  Solomon Eyal Shimony,et al.  Finding MAPs for Belief Networks is NP-Hard , 1994, Artif. Intell..

[38]  D. C. Wilkins,et al.  Stochastic Greedy Search: Efficiently Computing a Most Probable Explanation in Bayesian Networks , 2000 .

[39]  David C. Wilkins,et al.  Efficient Bayesian Network Inference: Genetic Algorithms, Stochastic Local Search, and Abstraction , 1999 .

[40]  V. Kulkarni.  Modeling, Analysis, Design, and Control of Stochastic Systems , 2000 .

[41]  Van Nostrand,et al.  Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm , 1967 .

[42]  David Maxwell Chickering,et al.  A Bayesian Approach to Tackling Hard Computational Problems (Preliminary Report) , 2001, Electron. Notes Discret. Math..

[43]  David C. Wilkins,et al.  Raven: Bayesian Networks for Human-Computer Intelligent Interaction , 2002 .

[44]  James D. Park.  Using weighted MAX-SAT engines to solve MPE , 2002, AAAI/IAAI.

[45]  L. Goddard.  Information Theory , 1962, Nature.

[46]  Nils J. Nilsson,et al.  Artificial Intelligence , 1974, IFIP Congress.

[47]  A. P. Dawid,et al.  Applications of a general propagation algorithm for probabilistic expert systems , 1992 .

[48]  Eric Horvitz,et al.  Restart Policies with Dependence among Runs: A Dynamic Programming Approach , 2002, CP.

[49]  Kalyanmoy Deb,et al.  Analyzing Deception in Trap Functions , 1992, FOGA.

[50]  A. Darwiche,et al.  Complexity Results and Approximation Strategies for MAP Explanations , 2011, J. Artif. Intell. Res..

[51]  Andrew J. Parkes,et al.  Tuning Local Search for Satisfiability Testing , 1996, AAAI/IAAI, Vol. 1.

[52]  Holger H. Hoos,et al.  A mixture-model for the behaviour of SLS algorithms for SAT , 2002, AAAI/IAAI.

[53]  Hector J. Levesque,et al.  Hard and Easy Distributions of SAT Problems , 1992, AAAI.

[54]  Thomas Stützle,et al.  Towards a Characterisation of the Behaviour of Stochastic Local Search Algorithms for SAT , 1999, Artif. Intell..

[55]  Gregory F. Cooper,et al.  The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks , 1990, Artif. Intell..

[56]  David C. Wilkins,et al.  Controlled generation of hard and easy Bayesian networks: Impact on maximal clique size in tree clustering , 2006, Artif. Intell..

[57]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.