Add to ORKGAn architecture for unifying various algorithms for probabilistic rea-soning is presented. Any algorithms having anytime, anywhere charac-teristics may be mixed in this scheme. Since algorithms for probabilistic reasoning have widely different behavior over classes of Bayes networks, the scheme permits taking advantage of the set of algorithms that hap-pen to perform well for the problem instance at hand. We concentrate on belief updating and belief revision. Some results are presented for our system (OVERMIND) consisting of several genetic algorithm instances, A*, etc. running in parallel
Behavioral experiments on humans and animals suggest that the brain performs probabilistic inference...
This paper investigates a representation language with flexibility inspired by probabilistic logic a...
The authors have constructed an iterative, probabilistic reasoning architecture for classification p...
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretic...
Probabilistic logic programs [4] combine the power of a pro- gramming language with a possible world...
This paper describes a class of probabilistic approximation algorithms based on bucket elimination w...
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretic...
We describe how to combine probabilistic logic and Bayesian networks to obtain a new frame-work ("Ba...
Computation of marginal probabilities in Bayesian Belief Networks is central to many probabilistic r...
The language of first-order logic, though successfully used in many applications, is not powerful en...
AbstractBelief updating in Bayes nets, a well-known computationally hard problem, has recently been ...
Abstract. This paper investigates the power of first-order probabilistic logic (FOPL) as a represent...
Given the complexity of the domains for which we would like to use computers as reasoning engines, ...
Abstract. This paper investigates the power of first-order probabilistic logic (FOPL) as a represent...
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Comput...
Behavioral experiments on humans and animals suggest that the brain performs probabilistic inference...
This paper investigates a representation language with flexibility inspired by probabilistic logic a...
The authors have constructed an iterative, probabilistic reasoning architecture for classification p...
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretic...
Probabilistic logic programs [4] combine the power of a pro- gramming language with a possible world...
This paper describes a class of probabilistic approximation algorithms based on bucket elimination w...
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretic...
We describe how to combine probabilistic logic and Bayesian networks to obtain a new frame-work ("Ba...
Computation of marginal probabilities in Bayesian Belief Networks is central to many probabilistic r...
The language of first-order logic, though successfully used in many applications, is not powerful en...
AbstractBelief updating in Bayes nets, a well-known computationally hard problem, has recently been ...
Abstract. This paper investigates the power of first-order probabilistic logic (FOPL) as a represent...
Given the complexity of the domains for which we would like to use computers as reasoning engines, ...
Abstract. This paper investigates the power of first-order probabilistic logic (FOPL) as a represent...
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Comput...
Behavioral experiments on humans and animals suggest that the brain performs probabilistic inference...
This paper investigates a representation language with flexibility inspired by probabilistic logic a...
The authors have constructed an iterative, probabilistic reasoning architecture for classification p...