Communications of the ACM
Foundations of logic programming
Foundations of logic programming
The art of Prolog: advanced programming techniques
The art of Prolog: advanced programming techniques
Generalized subsumption and its applications to induction and redundancy
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Explanation-based learning: a survey of programs and perspectives
ACM Computing Surveys (CSUR)
A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Induction as nonmonotonic inference
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Learning to Perceive and Act by Trial and Error
Machine Learning
Probabilistic logic programming
Information and Computation
Efficient learning of context-free grammars from positive structural examples
Information and Computation
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Modeling a dynamic and uncertain world I: symbolic and probabilistic reasoning about change
Artificial Intelligence
Simply logical: intelligent reasoning by example
Simply logical: intelligent reasoning by example
ACM SIGART Bulletin
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
First-order jk-clausal theories are PAC-learnable
Artificial Intelligence
Real-world applications of Bayesian networks
Communications of the ACM
The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Elements of machine learning
The justification of logical theories based on data compression
Machine intelligence 13
On the hardness of approximate reasoning
Artificial Intelligence
Using hidden nodes in Bayesian networks
Artificial Intelligence
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Abstraction and approximate decision-theoretic planning
Artificial Intelligence
Machine Learning - special issue on inductive logic programming
Answering queries from context-sensitive probabilistic knowledge bases
Selected papers from the international workshop on Uncertainty in databases and deductive systems
The independent choice logic for modelling multiple agents under uncertainty
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Logical settings for concept-learning
Artificial Intelligence
Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Enhanced hypertext categorization using hyperlinks
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Top-down induction of first-order logical decision trees
Artificial Intelligence
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Learning in graphical models
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
A unifying review of linear Gaussian models
Neural Computation
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Toward a Model of Intelligence as an Economy of Agents
Machine Learning
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Learning to construct knowledge bases from the World Wide Web
Artificial Intelligence - Special issue on Intelligent internet systems
Artificial Intelligence
Relational reinforcement learning
Machine Learning - Special issue on inducive logic programming
Relational instance-based learning with lists and terms
Machine Learning - Special issue on inducive logic programming
Machine Learning - Special issue on inducive logic programming
ACM SIGKDD Explorations Newsletter
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Expert Systems: Principles and Case Studies
Expert Systems: Principles and Case Studies
Algorithmic Program DeBugging
Machine Learning
Parameter Estimation in Stochastic Logic Programs
Machine Learning
Inductive Logic Programming: From Machine Learning to Software Engineering
Inductive Logic Programming: From Machine Learning to Software Engineering
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Relational Data Mining
How to upgrade propositional learners to first order logic: case study
Relational Data Mining
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Parameter Learning in Object-Oriented Bayesian Networks
Annals of Mathematics and Artificial Intelligence
Stochastic Inference of Regular Tree Languages
Machine Learning
Hidden Markov Models for Text Categorization in Multi-Page Documents
Journal of Intelligent Information Systems
The Challenges of Real-Time AI
Computer
Predicate Invention in ILP - an Overview
ECML '93 Proceedings of the European Conference on Machine Learning
Learning Belief Networks in the Presence of Missing Values and Hidden Variables
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Using Reinforcement Learning to Spider the Web Efficiently
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Using the Fisher Kernel Method to Detect Remote Protein Homologies
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
A Generalized Hidden Markov Model for the Recognition of Human Genes in DNA
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
Hidden Markov Model} Induction by Bayesian Model Merging
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Probabilistic Logic Programming and Bayesian Networks
ACSC '95 Proceedings of the 1995 Asian Computing Science Conference on Algorithms, Concurrency and Knowledge
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Structural Learning in Object Oriented Domains
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
Learning the Dimensionality of Hidden Variables
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Predicting UNIX Command Lines: Adjusting to User Patterns
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Lookahead and Discretization in ILP
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Term Comparisons in First-Order Similarity Measures
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Relational Markov models and their application to adaptive web navigation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamic Programming
Notes on methods based on maximum-likelihood estimation for learning the parameters of the mixture of Gaussians model
Equivalence notions and model minimization in Markov decision processes
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Solving factored MDPs using non-homogeneous partitions
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Bayesian Logic Programs
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
Probabilistic reasoning for complex systems
Probabilistic reasoning for complex systems
Learning statistical models from relational data
Learning statistical models from relational data
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Learning probabilistic models of link structure
The Journal of Machine Learning Research
Query transformations for improving the efficiency of ilp systems
The Journal of Machine Learning Research
Learning relational probability trees
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGKDD Explorations Newsletter
Stochastic attribute-value grammars
Computational Linguistics
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning associative Markov networks
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Kernels and Distances for Structured Data
Machine Learning
Naive Bayesian Classification of Structured Data
Machine Learning
Dependency Networks for Relational Data
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Relating probabilistic grammars and automata
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Balios: the engine for Bayesian logic programs
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
The Penn Treebank: annotating predicate argument structure
HLT '94 Proceedings of the workshop on Human Language Technology
An MDP-Based Recommender System
The Journal of Machine Learning Research
Learning the structure of Markov logic networks
ICML '05 Proceedings of the 22nd international conference on Machine learning
Fast inference and learning in large-state-space HMMs
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning structured prediction models: a large margin approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
Machine Learning
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A variational learning algorithm for the abstract hidden Markov model
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Towards learning stochastic logic programs from proof-banks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
nFOIL: integrating Naïve Bayes and FOIL
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Discriminative training of Markov logic networks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Learning planning rules in noisy stochastic worlds
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
Learning probabilities for noisy first-order rules
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Improving the efficiency of inductive logic programming through the use of query packs
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Cached sufficient statistics for efficient machine learning with large datasets
Journal of Artificial Intelligence Research
Policy recognition in the abstract hidden Markov model
Journal of Artificial Intelligence Research
Parameter learning of logic programs for symbolic-statistical modeling
Journal of Artificial Intelligence Research
First-order probabilistic inference
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Dynamic probabilistic relational models
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Generalizing plans to new environments in relational MDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Symbolic dynamic programming for first-order MDPs
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
IBAL: a probabilistic rational programming language
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Probabilistic classification and clustering in relational data
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Location-based activity recognition using relational Markov networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Lifted first-order probabilistic inference
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
BLOG: probabilistic models with unknown objects
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Relational object maps for mobile robots
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Learning structure and parameters of stochastic logic programs
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Revision of first-order Bayesian classifiers
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Loglinear models for first-order probabilistic reasoning
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Accelerating EM: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
The thing that we tried didn't work very well: deictic representation in reinforcement learning
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Discriminative probabilistic models for relational data
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Inductive policy selection for first-order MDPs
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Update rules for parameter estimation in Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Critical remarks on single link search in learning belief networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Fisher kernels for relational data
ECML'06 Proceedings of the 17th European conference on Machine Learning
CLP(BN): constraint logic programming for probabilistic knowledge
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
An integrated approach to learning bayesian networks of rules
ECML'05 Proceedings of the 16th European conference on Machine Learning
Learning models of relational stochastic processes
ECML'05 Proceedings of the 16th European conference on Machine Learning
Logical bayesian networks and their relation to other probabilistic logical models
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Probabilistic first-order theory revision from examples
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Deriving a stationary dynamic bayesian network from a logic program with recursive loops
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Classifying relational data with neural networks
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
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It has been a great pleasure to be asked to write the preface for the book based on Kristian Kersting's thesis. There is no doubt in my mind that this is a remarkable and outstanding piece of work. In his thesis Kristian has made an assault on one of the hardest integration problems at the heart of Artificial Intelligence research. This involves taking three disparate major areas of research and attempting a fusion among them. The three areas are: Logic Programming, Uncertainty Reasoning and Machine Learning. Every one of these is a major sub-area of research with its own associated international research conferences. Having taken on such a Herculean task, Kristian has produced a series of widely published results which are now at the core of a newly emerging area: Probabilistic Inductive Logic Programming. The new area is closely tied to, though strictly subsumes, a new field known as “Statistical Relational Learning” which has in the last few years gained major prominence in the American Artificial Intelligence research community. Within his thesis Kristian makes several major contributions, many of which have already been published in refereed conference and journal papers. Firstly, Kristian introduces a series of definitions which circumscribe the new area formed by extending Inductive Logic Programming to the case in which clauses are annotated with probability values. This represents a new and powerful framework which supersedes a number of influential papers and research areas in Artificial Intelligence. Secondly, Kristian introduces Bayesian Logic Programs (BLPs). These represent an elegantly defined lifting of Judea Pearl's Bayesian networks to the logic programming level. Since Kristian's introduction of BLPs, a number of results indicate that BLPs generalise many previously defined representations, not the least of which are Bayesian networks, Logic Programs, Probabilistic Relation Models and Stochastic Logic Programs. Next Kristian investigates the approach of Learning from proofs. This is an interesting new learning framework which is the first to go beyond the two standard semantic frameworks of Inductive Logic Programming. Kristian then looks at the problem of upgrading HMMs to logical HMMs. Hidden Markov Models (HMMs) are one of the most widely used machine learning technologies in Statistical Linguistics and Bioinformatics, and allow the representation of probabilistic finite automata. Kristian has upgraded standard HMMs to allow relational descriptions to be included within the description of the automata. The three standard HMM estimation algorithms are also upgraded. He has demonstrated the power of such representations using biological predictive modelling problems, and shown performance increases over alternative approaches. Kristian next considers the issue of upgrading Fisher Kernels to Relational Fisher kernels. Fisher kernels have been widely used within statistics and more recently in support vector machines. Building on his previous approaches involving lifting propositional representations Kristian shows how relations can be usefully included within this context. The approach was empirically tested on protein fold prediction and shown to have high predictive accuracy relative to logical HMMs. Lastly, Kristian introduces Markov decision programs. As a final demonstration of his general approach Kristian shows how temporal descriptions involving action can be introduced by lifting Markov decision processes to logical Markov decision programs. Kristian demonstrates how these can be learned using relational reinforcement algorithms which he tests empirically in a Blocks World setting. In summary, this thesis represents an extremely powerful and wide-ranging study which has made strong contributions right across the intellectual landscape. Both Kristian and his thesis supervisor Luc De Raedt, should be highly commended for this important contribution. London, July 2006, Stephen H. Muggleton