Probabilistic logic programming for hybrid relational domains

[1]  Luc De Raedt,et al.  Application of Dynamic Distributional Clauses for multi-hypothesis initialization in model-based object tracking , 2015, 2014 International Conference on Computer Vision Theory and Applications (VISAPP).

[2]  Luc De Raedt,et al.  Distributional Clauses Particle Filter , 2014, ECML/PKDD.

[3]  Luc De Raedt,et al.  Relational object tracking and learning , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[4]  Frank D. Wood,et al.  A New Approach to Probabilistic Programming Inference , 2014, AISTATS.

[5]  Enza Messina,et al.  A Particle Filtering Approach for Tracking an Unknown Number of Objects with Dynamic Relations , 2014, J. Math. Model. Algorithms Oper. Res..

[6]  Luc De Raedt,et al.  A particle filter for hybrid relational domains , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Kuo-Chu Chang,et al.  Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks , 2013, UAI.

[8]  Henry A. Kautz,et al.  Slice Normalized Dynamic Markov Logic Networks , 2012, NIPS.

[9]  Michael Beetz,et al.  Cognition-Enabled Autonomous Robot Control for the Realization of Home Chore Task Intelligence , 2012, Proceedings of the IEEE.

[10]  L. De Raedt,et al.  Logical Hidden Markov Models , 2011, J. Artif. Intell. Res..

[11]  Jaesik Choi,et al.  Lifted Relational Kalman Filtering , 2011, IJCAI.

[12]  Luc De Raedt,et al.  Under Consideration for Publication in Theory and Practice of Logic Programming the Magic of Logical Inference in Probabilistic Programming , 2022 .

[13]  J. Kadane.  Principles of Uncertainty , 2011 .

[14]  Luc De Raedt,et al.  Stochastic relational processes: Efficient inference and applications , 2011, Machine Learning.

[15]  Nicholas G. Polson,et al.  Particle learning for general mixtures , 2010 .

[16]  Nicholas G. Polson,et al.  Particle Learning and Smoothing , 2010, 1011.1098.

[17]  Moritz Tenorth,et al.  KNOWROB — knowledge processing for autonomous personal robots , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Nir Friedman,et al.  Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning , 2009 .

[19]  C. Lemieux.  Monte Carlo and Quasi-Monte Carlo Sampling , 2009 .

[20]  Luc De Raedt,et al.  On the Efficient Execution of ProbLog Programs , 2008, ICLP.

[21]  Weng-Keen Wong,et al.  Logical Hierarchical Hidden Markov Models for Modeling User Activities , 2008, ILP.

[22]  Eyal Amir,et al.  Sampling First Order Logical Particles , 2008, UAI.

[23]  Joshua B. Tenenbaum,et al.  Church: a language for generative models , 2008, UAI.

[24]  Ben Taskar,et al.  Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) , 2007 .

[25]  A. Doucet,et al.  On-Line Parameter Estimation in General State-Space Models , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[26]  Eyal Amir,et al.  First-Order Logical Filtering , 2005, IJCAI.

[27]  Stuart J. Russell,et al.  BLOG: Probabilistic Models with Unknown Objects , 2005, IJCAI.

[28]  Christian P. Robert,et al.  Monte Carlo Statistical Methods (Springer Texts in Statistics) , 2005 .

[29]  Leonid Peshkin,et al.  Factored Particles for Scalable Monitoring , 2002, UAI.

[30]  Geir Storvik,et al.  Particle filters for state-space models with the presence of unknown static parameters , 2002, IEEE Trans. Signal Process..

[31]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[32]  Nando de Freitas,et al.  Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks , 2000, UAI.

[33]  M. Pitt,et al.  Filtering via Simulation: Auxiliary Particle Filters , 1999 .

[34]  Krzysztof R. Apt,et al.  From logic programming to Prolog , 1996, Prentice Hall International series in computer science.

[35]  G. Kitagawa.  Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[36]  G. Casella,et al.  Rao-Blackwellisation of sampling schemes , 1996 .

[37]  John W. Lloyd,et al.  Partial Evaluation in Logic Programming , 1991, J. Log. Program..

[38]  Ulf Nilsson,et al.  Logic, programming and Prolog , 1990 .

[39]  J. W. Lloyd,et al.  Foundations of logic programming; (2nd extended ed.) , 1987 .

[40]  R. Ramakrishnan,et al.  An amateur's introduction to recursive query processing strategies , 1986, SIGMOD '86.

[41]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[42]  M. J. Bayarri,et al.  Particle Learning for Sequential Bayesian Computation , 2010 .

[43]  Subrata Das,et al.  Factored reasoning for monitoring dynamic team and goal formation , 2009, Inf. Fusion.

[44]  Arnaud Doucet,et al.  An overview of sequential Monte Carlo methods for parameter estimation in general state-space models , 2009 .

[45]  Luc De Raedt,et al.  Probabilistic Inductive Logic Programming - Theory and Applications , 2008, Probabilistic Inductive Logic Programming.

[46]  Wolfram Burgard,et al.  A Probabilistic Relational Model for Characterizing Situations in Dynamic Multi-Agent Systems , 2007, GfKl.

[47]  Leslie Pack Kaelbling,et al.  Logical Particle Filtering , 2007, Probabilistic, Logical and Relational Learning - A Further Synthesis.

[48]  Andrew McCallum,et al.  Introduction to Statistical Relational Learning , 2007 .

[49]  Ben Taskar,et al.  Introduction to statistical relational learning , 2007 .

[50]  Stuart J. Russell,et al.  Probabilistic models with unknown objects , 2006 .

[51]  Stuart J. Russell,et al.  Approximate Inference for Infinite Contingent Bayesian Networks , 2005, AISTATS.

[52]  Stuart J. Russell,et al.  Dynamic bayesian networks: representation, inference and learning , 2002 .

[53]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[54]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[55]  W. Gilks,et al.  Following a moving target—Monte Carlo inference for dynamic Bayesian models , 2001 .

[56]  Kitagawa.  Self organizing Time Series Model , 2001 .

[57]  Tomoyuki Higuchi,et al.  Self-Organizing Time Series Model , 2001, Sequential Monte Carlo Methods in Practice.

[58]  Taisuke Sato,et al.  A Statistical Learning Method for Logic Programs with Distribution Semantics , 1995, ICLP.

[59]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[60]  Teodor C. Przymusinski.  Perfect Model Semantics , 1988, ICLP/SLP.

[61]  J. Lloyd.  Foundations of Logic Programming , 1984, Symbolic Computation.