Add to ORKGProbabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provi
International audiencePossibilistic logic (PL) is more than thirty years old. The paper proposes a s...
We propose a general scheme for adding probabilistic reasoning capabilities to any knowledge represe...
Belief networks, also called Bayesian networks or probabilistic causal networks, were developed in t...
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretic...
Reasoning with uncertain information has received a great deal of attention recently, as this issue ...
International audienceDue to its major focus on knowledge representation and reasoning, artificial i...
This article aims to achieve two goals: to show that probability is not the only way of dealing with...
Abstract. Although probabilistic knowledge representations and probabilistic reasoning have by now s...
Although probabilistic knowledge representations and probabilistic reasoning have by now secured the...
This research addresses two intensive computational problems of reasoning under uncertainty in artif...
We describe how to combine probabilistic logic and Bayesian networks to obtain a new frame-work ("Ba...
This comprehensive book describes Probabilistic Logic Networks (PLN), a novel conceptual, mathematic...
AbstractProbability is usually closely related to Boolean structures, i.e., Boolean algebras or prop...
We propose a general scheme for adding probabilistic reasoning capabilities to a wide variety of kno...
Probability is usually closely related to Boolean structures, i.e. Boolean algebras or propositional...
International audiencePossibilistic logic (PL) is more than thirty years old. The paper proposes a s...
We propose a general scheme for adding probabilistic reasoning capabilities to any knowledge represe...
Belief networks, also called Bayesian networks or probabilistic causal networks, were developed in t...
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretic...
Reasoning with uncertain information has received a great deal of attention recently, as this issue ...
International audienceDue to its major focus on knowledge representation and reasoning, artificial i...
This article aims to achieve two goals: to show that probability is not the only way of dealing with...
Abstract. Although probabilistic knowledge representations and probabilistic reasoning have by now s...
Although probabilistic knowledge representations and probabilistic reasoning have by now secured the...
This research addresses two intensive computational problems of reasoning under uncertainty in artif...
We describe how to combine probabilistic logic and Bayesian networks to obtain a new frame-work ("Ba...
This comprehensive book describes Probabilistic Logic Networks (PLN), a novel conceptual, mathematic...
AbstractProbability is usually closely related to Boolean structures, i.e., Boolean algebras or prop...
We propose a general scheme for adding probabilistic reasoning capabilities to a wide variety of kno...
Probability is usually closely related to Boolean structures, i.e. Boolean algebras or propositional...
International audiencePossibilistic logic (PL) is more than thirty years old. The paper proposes a s...
We propose a general scheme for adding probabilistic reasoning capabilities to any knowledge represe...
Belief networks, also called Bayesian networks or probabilistic causal networks, were developed in t...