Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The independent choice logic for modelling multiple agents under uncertainty
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Parameter Estimation in Stochastic Logic Programs
Machine Learning
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Bayesian Logic Programs
FreeEnCal: A Forward Reasoning Engine with General-Purpose
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Proceedings of the 2006 conference on Information Modelling and Knowledge Bases XVII
PRISM: a language for symbolic-statistical modeling
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Causality: Models, Reasoning and Inference
Causality: Models, Reasoning and Inference
Logical bayesian networks and their relation to other probabilistic logical models
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
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In orders to deal with uncertainty by systematical methodologies, some structural models combining probability theory with logic systems have been proposed. However, these models used only the formal language part of the underlying logic system to represent empirical knowledge of target domains, but not asked the logical consequence theory part of the underlying logic system to reason about empirical theorems that are logically implied in domain knowledge. As the first step to establish a unifying framework to support uncertainty reasoning, this paper proposes a new framework that extends and formalizes traditional Bayesian networks by combining Bayesian networks with strong relevant logic. The most intrinsic feature of the framework is that it provides a formal system for representing and reasoning about generalized Bayesian networks, and therefore, within the framework, for given empirical knowledge in a specific target domain, one can reason out those new empirical theorems that are certainly relevant to given empirical knowledge. As a result, using an automated forward reasoning engine based on strong relevant logic, it is possible to get Bayesian networks semi-automatically.