Artificial Intelligence
Incidence calculus: A mechanism for probabilistic reasoning
Journal of Automated Reasoning
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
A logic for reasoning about probabilities
Information and Computation - Selections from 1988 IEEE symposium on logic in computer science
An introduction to possibilistic and fuzzy logics
Readings in uncertain reasoning
Dempster's rule of combination is #P-complete (research note)
Artificial Intelligence
Abductive inference models for diagnostic problem-solving
Abductive inference models for diagnostic problem-solving
An analysis of first-order logics of probability
Artificial Intelligence
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
A catalog of complexity classes
Handbook of theoretical computer science (vol. A)
Theory of generalized annotated logic programming and its applications
Journal of Logic Programming
Probabilistic logic programming
Information and Computation
Handbook of logic in artificial intelligence and logic programming (vol. 3)
International Journal of Human-Computer Studies
Can we enforce full compositionality in uncertainty calculi?
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
On the hardness of approximate reasoning
Artificial Intelligence
The uncertain reasoner's companion: a mathematical perspective
The uncertain reasoner's companion: a mathematical perspective
Answering queries from context-sensitive probabilistic knowledge bases
Selected papers from the international workshop on Uncertainty in databases and deductive systems
Probabilistic frame-based systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Towards a standard upper ontology
Proceedings of the international conference on Formal Ontology in Information Systems - Volume 2001
Nonserial Dynamic Programming
Possibility Theory, Probability Theory and Multiple-Valued Logics: A Clarification
Annals of Mathematics and Artificial Intelligence
The Method of Assigning Incidences
Applied Intelligence
Annotated Logic Applications for Imperfect Information
Applied Intelligence
A Symbolic Approach To Uncertainty Management
Applied Intelligence
Towards a Possibilistic Logic Handling of Preferences
Applied Intelligence
IEEE Transactions on Knowledge and Data Engineering
Learning Probabilistic Models of Relational Structure
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Many-Valued and Annotated Modal Logics
ISMVL '98 Proceedings of the The 28th International Symposium on Multiple-Valued Logic
Bayesian Logic Programs
Learning probabilistic models of link structure
The Journal of Machine Learning Research
Reasoning about Uncertainty
A Prototypical System for Soft Evidential Update
Applied Intelligence
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences - Volume 09
Machine Learning
The design and implementation of VAMPIRE
AI Communications - CASC
Probabilistic description logic programs
International Journal of Approximate Reasoning
Parameter learning for relational Bayesian networks
Proceedings of the 24th international conference on Machine learning
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
A Hole in Goal Trees: Some Guidance from Resolution Theory
IEEE Transactions on Computers
MEBN: A language for first-order Bayesian knowledge bases
Artificial Intelligence
Discriminative structure and parameter learning for Markov logic networks
Proceedings of the 25th international conference on Machine learning
Probabilistic description logic programs under inheritance with overriding for the Semantic Web
International Journal of Approximate Reasoning
Structured machine learning: the next ten years
Machine Learning
First-Order Probabilistic Languages: Into the Unknown
Inductive Logic Programming
Efficient Weight Learning for Markov Logic Networks
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Discriminative Structure Learning of Markov Logic Networks
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Modeling and Reasoning with Bayesian Networks
Modeling and Reasoning with Bayesian Networks
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Learning Markov logic network structure via hypergraph lifting
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Learning directed probabilistic logical models: ordering-search versus structure-search
Annals of Mathematics and Artificial Intelligence
Max-Margin Weight Learning for Markov Logic Networks
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Discriminative training of Markov logic networks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Reasoning within fuzzy description logics
Journal of Artificial Intelligence Research
Reasoning with very expressive fuzzy description logics
Journal of Artificial Intelligence Research
Theorem proving under uncertainty: a possibility theory-based approach
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
Annals of Mathematics and Artificial Intelligence
A formal framework for description logics with uncertainty
International Journal of Approximate Reasoning
Basic principles of learning Bayesian logic programs
Probabilistic inductive logic programming
The independent choice logic and beyond
Probabilistic inductive logic programming
Design Principles for Ontological Support of Bayesian Evidence Management
Proceedings of the 2010 conference on Ontologies and Semantic Technologies for Intelligence
MAP complexity results and approximation methods
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Network fragments: representing knowledge for constructing probabilistic models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Logical bayesian networks and their relation to other probabilistic logical models
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Multi levels semantic architecture for multimodal interaction
Applied Intelligence
Hi-index | 0.00 |
Formal logical tools are able to provide some amount of reasoning support for information analysis, but are unable to represent uncertainty. Bayesian network tools represent probabilistic and causal information, but in the worst case scale as poorly as some formal logical systems and require specialized expertise to use effectively. We describe a framework for systems that incorporate the advantages of both Bayesian and logical systems. We define a formalism for the conversion of automatically generated natural deduction proof trees into Bayesian networks. We then demonstrate that the merging of such networks with domain-specific causal models forms a consistent Bayesian network with correct values for the formulas derived in the proof. In particular, we show that hard evidential updates in which the premises of a proof are found to be true force the conclusions of the proof to be true with probability one, regardless of any dependencies and prior probability values assumed for the causal model. We provide several examples that demonstrate the generality of the natural deduction system by using inference schemes not supportable directly in Horn clause logic. We compare our approach to other ones, including some that use non-standard logics.