Thomas Lukasiewicz. Probabilistic logic programming. In Proc. of the 13th European Conf. on Artificial Intelligence (ECAI-98), pages 388--392, Brighton (England), August 1998.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....formalisms, depending on the meaning and the nature of the quantitative information to be represented, and on the class of reasoning tasks to be performed. Some formalisms allow the quantitative information to be associated with rules (e.g. 113] others with rule heads or rule bodies (e.g. [121, 109, 110]) or with single atoms of the language (e.g. 117] or with formulas (e.g. 118] The information is sometimes expressed by means of a single value (e.g. 113] sometimes in the form of ranges or intervals of values (e.g. 118, 119, 121, 109, 110] maybe allowing variables to appear in ....

.... [113] others with rule heads or rule bodies (e.g. 121, 109, 110] or with single atoms of the language (e.g. 117] or with formulas (e.g. 118] The information is sometimes expressed by means of a single value (e.g. 113] sometimes in the form of ranges or intervals of values (e.g. [118, 119, 121, 109, 110]) maybe allowing variables to appear in the definition of such intervals. The point is that quantitative information has been considered for addressing different problems, which impose different requirements, and thus lead unsurprisingly to different solutions and suggestions. Quantitative ....

[Article contains additional citation context not shown here]

T. Lukasiewicz. Probabilistic Logic Programming. In H. Prade, editor, Proceedings of the Thirteenth European Conference on Artificial Intelligence (ECAI'98), pages 388--392. John Wiley & Sons, Aug. 1998.

....involved. Hybrid Probabilistic Programs (HPPs) 2] represent one of the first frameworks that allow a logic program to explicitly encode a variety of different probability assumptions explicitly into the program, for use in inferencing. Most existing frameworks for uncertainty in logic programming [4, 5, 8, 9, 12, 14, 15, 17, 19, 21, 7] do not permit this. A few important initial attempts to incorporate different probabilistic strategies were made by Thone et al. 22] and Lakshmanan [12] which culminated in an extension of the relational algebra that accommodated different probabilistic strategies [10] In this paper, we have ....

T. Lukasiewicz. (1998) Probabilistic Logic Programming, in Procs. 13th biennial European Conference on Artificial Intelligence, pps 388392, Brighton, UK, August 1998.

....computing is not a single methodology, but a partnership of fuzzy logic, probabilistic reasoning and neuro computing ( 38] and in this paper we consider only the first two partners. Fuzzy logic and probabilistic logic have been applied to extend both logic programs (e.g. 15] 24] 29] 32] [28]) and object oriented models (e.g. 7] 33] 36] 14] to deal with vagueness and uncertainty often encountered in practical problems. However, research on combining all together logic programming, object oriented programming and soft computing appears to be sporadic. The benefit of such a ....

Lukasiewicz, T. 1998. Probabilistic logic programming. In Proceedings of the 13 th Biennial European Conference on Artificial Intelligence, pp. 388-392.

....latter are appropriate for describing statistical knowledge. In the present paper, we assume that probabilities are de ned over a set of possible worlds. Probabilistic reasoning in its full generality is a quite tricky task and very di erent from classical reasoning (see especially [18] 15] and [14]) It should generally be performed by global linear programming methods, rather than by local inference techniques. For this reason, it is generally also computationally more complex than classical reasoning. In particular, the model and xpoint characterization and the proof theory of classical ....

....For this reason, it is generally also computationally more complex than classical reasoning. In particular, the model and xpoint characterization and the proof theory of classical de nite logic programming generally do not carry over to probabilistic de nite logic programming (as presented in [14]) Moreover, the tractability of special cases of classical logic programming generally does not carry over to the corresponding special cases of probabilistic logic programming. However, we would like an approach to many valued disjunctive logic programming that does not ignore the years of work ....

[Article contains additional citation context not shown here]

T. Lukasiewicz. Probabilistic logic programming. In Proc. of the 13th Biennial European Conf. on Articial Intelligence, pages 388-392. J. Wiley & Sons, 1998.

....over the domain, while Subrahmanian and his group [53, 54, 8, 9] focus on annotation based approaches to degrees of belief. Another approach to probabilistic logic programming with degrees of belief, which is especially directed towards efficient implementations, has been recently introduced in [40, 45]. The following shows a very simple probabilistic logic program as in [40, 45] which expresses that all penguins are birds and that birds have legs with a probability of at least 0.98 : 9 22175 41278 7 Nearly all the above approaches of integrating logic and probability are based on ....

....approaches to degrees of belief. Another approach to probabilistic logic programming with degrees of belief, which is especially directed towards efficient implementations, has been recently introduced in [40, 45] The following shows a very simple probabilistic logic program as in [40, 45], which expresses that all penguins are birds and that birds have legs with a probability of at least 0.98 : 9 22175 41278 7 Nearly all the above approaches of integrating logic and probability are based on the notion of modeltheoretic logical entailment. This notion, however, has ....

T. Lukasiewicz. Probabilistic logic programming. In Proceedings ECAI-98, pages 388--392. Wiley & Sons, 1998.

....edge bases (see Section 9) Moreover, the new notions of entailment are generally much stronger than the classical notion of logical entailment based on conditioning. Thus, they may especially be useful where classical logical entailment is too weak, for example, in probabilistic logic programming [54, 55]. The main contributions of this paper can be briefly summarized as follows: We show that neither the classical notion of logical entailment for conditional constraints nor the classical notion of entailment under coherence have the properties that are desired in reasoning from statistical and ....

T. Lukasiewicz. Probabilistic logic programming. In Proceedings of the 13th European Conference on Artificial Intelligence, pages 388--392. J. Wiley & Sons, 1998.

....of this paper. This work was supported by a DFG grant and the Austrian Science Fund Project N Z29 INF. This paper is a substantially extended and revised version of a paper that appeared in Proceedings of the 13th European Conference on Artificial Intelligence (ECAI 98) pages 388 392, 1998 [42]. Copyright c 2000 by the authors 2 INFSYS RR 1843 00 01 1 Introduction During the recent decades, the management of uncertainty has come to play an important role in knowledge representation and reasoning. Many different formalisms and methodologies have been proposed for handling uncertain ....

T. Lukasiewicz. Probabilistic logic programming. In Proceedings of the 13th European Conference on Artificial Intelligence, pages 388--392. J. Wiley & Sons, 1998.

.... about conditional constraints (Dubois Prade 1988; Dubois et al. 1990; 1993; Amarger et al. 1991; Jaumard et al. 1991; Thone et al. 1992; Frisch Haddawy 1994; Heinsohn 1994; Luo et al. 1996; Lukasiewicz 1999a; 1999b) and their generalizations, for example, to probabilistic logic programs (Lukasiewicz 1998). Now, the main idea of this paper is to use techniques for default reasoning from conditional knowledge bases in order to perform probabilistic reasoning from statistical knowledge and degrees of beliefs. More precisely, we extend the notions of entailment in system Z, Lehmann s lexicographic ....

Lukasiewicz, T. 1998. Probabilistic logic programming. In Proceedings ECAI-98, 388--392. J. Wiley & Sons.

.... express interval restrictions for conditional probabilities, also called conditional constraints [37] The literature contains extensive work on reasoning about conditional constraints (see especially [12, 2, 16, 34, 37] and their generalizations (for example, to probabilistic logic programs [33]) Now, the main idea of this paper is to use techniques for default reasoning from conditional knowledge bases in order to perform probabilistic reasoning from statistical knowledge and degrees of beliefs. More precisely, we extend the notion of entailment in system Z , Lehmann s lexicographic ....

T. Lukasiewicz. Probabilistic logic programming. In Proceedings of the 13th European Conference on Artificial Intelligence, pages 388--392. J. Wiley & Sons, 1998.

....the domain (see, for example, 2] and [15] The first ones are suitable for representing degrees of belief, while the latter are appropriate for describing statistical knowledge. The same classification holds for approaches to probabilistic logic programming (see, for example, 27] 26] 28] [24], and [25] Many approaches to truth functional finite valued logic programming are restricted to three or four truth values (see, for example, 18] 11] 12] 3] 30] and [8] Among these approaches, the one closest in spirit to n valued logic programming in L n is the 3 valued ....

....and also well grounded on a probabilistic semantics. Our n valued logic programming in L n is closely related to van Emden s infinite valued quantitative deduction in [34] More precisely, both approaches have a common semantic background in probabilistic logic programming as introduced in [24]. That is, interpreted by probability distributions over possible worlds that satisfy an extension of (1) the probabilistic logic programs in [24] coincide in their fixpoint semantics with n valued logic programs in L n (if the implication connective is interpreted as material implication) ....

[Article contains additional citation context not shown here]

T. Lukasiewicz. Probabilistic logic programming. In Proc. of the 13th European Conference on Artificial Intelligence, pages 388--392. J. Wiley & Sons, 1998.

.... Poole [22] Haddawy [8] and Jaeger [10] discuss approaches to degrees of belief close to Bayesian networks [21] Finally, another approach to probabilistic logic programming with degrees of belief, which is especially directed towards efficient implementations, has recently been introduced in [14]. Usually, the available probabilistic knowledge does not suffice to specify completely a distribution. In this case, applying the principle of maximum entropy is a well appreciated means of probabilistic inference, both from a statistical and from a logical point of view. Entropy is an ....

....suffer from efficiency problems (which are due to an exponential number of possible worlds in the number of propositional variables) In this paper, however, we will see that this is not the case. More precisely, we will show that the efficient approach to probabilistic logic programming in [14], combined with new ideas, can be extended to an efficient approach to probabilistic logic programming under maximum entropy. Roughly speaking, the probabilistic logic programs presented in [14] generally carry an additional structure that can successfully be exploited in both classical ....

[Article contains additional citation context not shown here]

T. Lukasiewicz. Probabilistic logic programming. In Proc. of the 13th European Conference on Artificial Intelligence, pages 388--392. J. Wiley & Sons, 1998.

....example, the requested least upper bound for u 1 = u 2 = u and x 1 = x 2 = x is shown in Fig. 5 for u; x 2 [0; 1] r 1 = r 2 = 0:15, v 1 = 0:8, y 1 = 0:8, and s 1 2 f0:05; 0:1g. The requested least upper bound for u 1 u 2 or x 1 x 2 is the maximum value over [u 1 ; u 2 ] Theta [x 1 ; x 2 ] Lukasiewicz s1=0.05 z2 0.99 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 u x s1=0.1 z2 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 ....

Lukasiewicz, T. (1998d). Probabilistic logic programming. In Proceedings of the 13th European Conference on Artificial Intelligence, pp. 388--392. J. Wiley & Sons.

....latter are appropriate for describing statistical knowledge. In the present paper, we assume that probabilities are de ned over a set of possible worlds. Probabilistic reasoning in its full generality is a quite tricky task and very di erent from classical reasoning (see especially [19] 15] and [14]) It should generally be performed by global linear programming methods, rather than by local inference techniques. For this reason, it is generally also computationally more complex than classical reasoning. In particular, the model and xpoint characterization and the proof theory of ....

....For this reason, it is generally also computationally more complex than classical reasoning. In particular, the model and xpoint characterization and the proof theory of classical de nite logic programming generally do not carry over to probabilistic de nite logic programming (as presented in [14]) Moreover, the tractability of special cases of classical logic programming generally does not carry over to the corresponding special cases of probabilistic logic programming. However, we would like an approach to many valued disjunctive logic programming that does not ignore the years of work ....

[Article contains additional citation context not shown here]

T. Lukasiewicz. Probabilistic logic programming. In Proc. of the 13th Biennial European Conf. on Articial Intelligence, pages 388-392. J. Wiley & Sons, 1998.

No context found.

T. Lukasiewicz. Probabilistic logic programming. In Proc. of the 13th European Conference on Artificial Intelligence, pages 388--392. J. Wiley & Sons, 1998. To appear.

....the domain (see, for example, 2] and [15] The first ones are suitable for representing degrees of belief, while the latter are appropriate for describing statistical knowledge. The same classification holds for approaches to probabilistic logic programming (see, for example, 27] 26] 28] [24], and [25] Many approaches to truth functional finite valued logic programming are restricted to three or four truth values (see, for example, 18] 11] 12] 3] 30] and [8] Among these approaches, the one closest in spirit to n valued logic programming in L n is the 3 valued ....

....and also well grounded on a probabilistic semantics. Our n valued logic programming in L n is closely related to van Emden s infinite valued quantitative deduction in [34] More precisely, both approaches have a common semantic background in probabilistic logic programming as introduced in [24]. That is, interpreted by probability distributions over possible worlds that satisfy an extension of (1) the probabilistic logic programs in [24] coincide in their fixpoint semantics with n valued logic programs in L n (if the implication connective is interpreted as material implication) ....

[Article contains additional citation context not shown here]

T. Lukasiewicz. Probabilistic logic programming. In Proc. of the 13th European Conference on Artificial Intelligence, pages 388--392. J. Wiley & Sons, 1998.

....example, the requested least upper bound for u 1 = u 2 = u and x 1 = x 2 = x is shown in Fig. 5 for u; x 2 [0; 1] r 1 = r 2 = 0:15, v 1 = 0:8, y 1 = 0:8, and s 1 2 f0:05; 0:1g. The requested least upper bound for u 1 u 2 or x 1 x 2 is the maximum value over [u 1 ; u 2 ] Theta [x 1 ; x 2 ] Lukasiewicz s1=0.05 z2 0.99 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 u x s1=0.1 z2 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 ....

Lukasiewicz, T. (1998d). Probabilistic logic programming. In Proceedings of the 13th European Conference on Artificial Intelligence, pp. 388--392. J. Wiley & Sons.

.... Poole [32] Haddawy [12] and Jaeger [14] discuss approaches to degrees of belief close to Bayesian networks [31] Finally, another approach to probabilistic logic programming with degrees of belief, which is especially directed towards efficient implementations, has recently been introduced in [20]. Usually, the available probabilistic knowledge does not suffice to specify completely a distribution. In this case, applying the principle of maximum entropy is a well appreciated means of probabilistic inference, both from a statistical and from a logical point of view. Entropy is an ....

....suffer from efficiency problems (which are due to an exponential number of possible worlds in the number of propositional variables) In this paper, however, we will see that this is not the case. More precisely, we will show that the efficient approach to probabilistic logic programming in [20], combined with new ideas, can be extended to an efficient approach to probabilistic logic programming under maximum entropy. Roughly speaking, the probabilistic logic programs presented in [20] generally carry an additional structure that can successfully be exploited in both classical ....

[Article contains additional citation context not shown here]

T. Lukasiewicz. Probabilistic logic programming. In Proceedings of the 13th European Conference on Artificial Intelligence, pages 388--392. J. Wiley & Sons, 1998.

No context found.

Thomas Lukasiewicz. Probabilistic logic programming. In Proc. of the 13th European Conf. on Artificial Intelligence (ECAI-98), pages 388--392, Brighton (England), August 1998.

No context found.

T. Lukasiewicz. Probabilistic logic programming. In Proc. of the 13th European Conf. on Artificial Intelligence (ECAI-98), pages 388--392, 1998.

No context found.

Thomas Lukasiewicz. Probabilistic logic programming. In Proc. of the 13th European Conf. on Artificial Intelligence (ECAI-98), pages 388--392, Brighton (England) , August 1998.

No context found.

T. Lukasiewicz. Probabilistic logic programming. In 13th European Conference on Artificial Intelligence, 388--392, 1998.

No context found.

T. Lukasiewicz. Probabilistic logic programming. In Proc. of the 13th European Conf. on Artificial Intelligence (ECAI-98), pages 388--392, 1998.

No context found.

Lukasiewicz, T. 1998. Probabilistic logic programming. In Prade, H., ed., Proceedings of the Thirteenth European Conference on Artificial Intelligence, 388--392. Brighton, UK: J. Wiley & Sons. Muggleton, S. 2002. Stochastic logic programs. Journal of Logic Programming. Forthcoming.

No context found.

T. Lukasiewicz. (1998) Probabilistic Logic Programming, in Procs. 13th biennial European Conference on Artificial Intelligence, pps 388-392, Brighton, UK, August 1998.

No context found.

T. Lukasiewicz. (1998) Probabilistic Logic Programming, in Procs. 13th biennial European Conference on Artificial Intelligence, pps 388-392, Brighton, UK, August 1998.

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