MEBN: A language for first-order Bayesian knowledge bases
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Multi-Entity Bayesian Networks is presented, a first-order language for specifying probabilistic knowledge bases as parameterized fragments of Bayesian networks, and a proof is given that MEBN can represent a probability distribution on interpretations of any finitely axiomatizable first- order theory.About:
This article is published in Artificial Intelligence.The article was published on 2008-02-01 and is currently open access. It has received 281 citations till now. The article focuses on the topics: Bayesian network & Knowledge representation and reasoning.read more
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KnowRob: A knowledge processing infrastructure for cognition-enabled robots
Moritz Tenorth,Michael Beetz +1 more
TL;DR: This article introduces the KnowRob knowledge processing system, a system specifically designed to provide autonomous robots with the knowledge needed for performing everyday manipulation tasks, and evaluates the system’s scalability and present different integrated experiments that show its versatility and comprehensiveness.
Book
Statistical Relational Artificial Intelligence: Logic, Probability, and Computation
TL;DR: This book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extensions of Bayesian networks.
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Probability and Statistics
TL;DR: In this paper, the authors introduce the concept of conditional probability and introduce a set of variables and distributions for estimating the probability of a given set of estimators, including large random samples and special distributions.
References
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Book
Artificial Intelligence: A Modern Approach
Stuart Russell,Peter Norvig +1 more
TL;DR: In this article, the authors present a comprehensive introduction to the theory and practice of artificial intelligence for modern applications, including game playing, planning and acting, and reinforcement learning with neural networks.
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Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
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A translation approach to portable ontology specifications
TL;DR: This paper describes a mechanism for defining ontologies that are portable over representation systems, basing Ontolingua itself on an ontology of domain-independent, representational idioms.
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Probability and Measure
TL;DR: In this paper, the convergence of distributions is considered in the context of conditional probability, i.e., random variables and expected values, and the probability of a given distribution converging to a certain value.