Abstract. Probabilistic logic programs have primarily studied the problem of entailment of probabilistic atoms. However, there are some interesting applications where we are interested in finding a possible world that is most probable. Our first result shows that the problem of computing such ”maximally probable worlds” (MPW) is intractable. We subsequently show that we can often greatly reduce the size of the linear program used in past work (by Ng and Subrahmanian) and yet solve the problem exactly. However, the intractability results still make computational efficiency quite impossible. We therefore also develop several heuristics to solve the MPW problem and report extensive experimental results on the accuracy and efficiency of such he...
In this paper, we focus on the combination of probabilistic logic programming with the principle of...
Consider a set of logical sentences together with probabilities that they are true. These probabilit...
In this paper, we focus on the combination of probabilistic logic programming with the principle of ...
Abstract. Probabilistic logic programs have primarily studied the problem of entailment of probabili...
We present a new approach to probabilistic logic programs with a possible worlds semantics. Classica...
We introduce a new approach to probabilistic logic programming in which probabilities are defined ov...
Life is uncertain, full of ambiguities. Paradoxically, only embracing stochasticity, not fighting ra...
AbstractSuppose we are given a set W of logical structures, or possible worlds, a set of logical for...
A central goal of AI is to reason efficiently in domains that are both complex and uncertain. Most a...
This paper is on the combination of two powerful approaches to uncertain reasoning: logic programmin...
Abstract. Two approaches to logic programming with probabilities emerged over time: bayesian reasoni...
As a framewrok for simple but basic statistical inference problems we introduce a genetic Most Likel...
Probabilistic logic programming is an effective formalism for encoding problems characterized by unc...
Model counting is the problem of computing the num-ber of models that satisfy a given propositional ...
This paper presents an approximate method for probabilistic entailment problem in knowledge bases wh...
In this paper, we focus on the combination of probabilistic logic programming with the principle of...
Consider a set of logical sentences together with probabilities that they are true. These probabilit...
In this paper, we focus on the combination of probabilistic logic programming with the principle of ...
Abstract. Probabilistic logic programs have primarily studied the problem of entailment of probabili...
We present a new approach to probabilistic logic programs with a possible worlds semantics. Classica...
We introduce a new approach to probabilistic logic programming in which probabilities are defined ov...
Life is uncertain, full of ambiguities. Paradoxically, only embracing stochasticity, not fighting ra...
AbstractSuppose we are given a set W of logical structures, or possible worlds, a set of logical for...
A central goal of AI is to reason efficiently in domains that are both complex and uncertain. Most a...
This paper is on the combination of two powerful approaches to uncertain reasoning: logic programmin...
Abstract. Two approaches to logic programming with probabilities emerged over time: bayesian reasoni...
As a framewrok for simple but basic statistical inference problems we introduce a genetic Most Likel...
Probabilistic logic programming is an effective formalism for encoding problems characterized by unc...
Model counting is the problem of computing the num-ber of models that satisfy a given propositional ...
This paper presents an approximate method for probabilistic entailment problem in knowledge bases wh...
In this paper, we focus on the combination of probabilistic logic programming with the principle of...
Consider a set of logical sentences together with probabilities that they are true. These probabilit...
In this paper, we focus on the combination of probabilistic logic programming with the principle of ...