T. Lukasiewicz. (1998) Magic Inference Rules for Probabilistic Deduction under Taxonomic Knowledge, Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pps 354-361, Madison, Wisconsin, USA, July 1998.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....and with logics of probability and time. In addition to the authors works, probabilistic logic programs were studied by Thone et al. 25] and Lakshmanan [17] who showed how different probabilistic dependencies can be encoded into logic programs. Kiessling s group [14, 25] and Lukasiewicz [21] made important contributions to bottom up computations of logic programs. The work reported in this paper may be viewed as an extension of the above works (as well as [23, 24, 2, 4] to handle temporal probabilistic information. In addition to the model theory, we have developed both bottom up ....

T. Lukasiewicz. (1998) Magic Inference Rules for Probabilistic Deduction under Taxonomic Knowledge, Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pps 354-361, Madison, Wisconsin, USA, July 1998.

....and with logics of probability and time. In addition to the authors works, probabilistic logic programs were studied by Thone et al. 25] and Lakshmanan [17] who showed how different probabilistic dependencies can be encoded into logic programs. Kiessling s group [14, 25] and Lukasiewicz [21] made important contributions to bottom up computations of logic programs. The work reported in this paper may be viewed as an extension of the above works (as well as [23, 24, 2, 4] to handle temporal probabilistic information. In addition to the model theory, we have developed both bottom up ....

T. Lukasiewicz. (1998) Magic Inference Rules for Probabilistic Deduction under Taxonomic Knowledge, Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pps 354-361, Madison, Wisconsin, USA, July 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 ....

....programs. Each of these algorithms is tuned to fit the class within which an HPP falls (i.e. class HPP 1 , HPP 2 or HPP r ; r 3) We have given algorithmic complexity analyses of these problems. To date, with the exception of the work by Kiessling s group [7, 22] and by Lukasiewicz [15], almost no work on bottom up algorithms for computing probabilistic logic programs exists. Our algorithms are the first to apply not only to HPPs, but to have finer complexity bounds for different classes of HPPs. Second, we have studied the computational complexity of the Entailment and ....

T. Lukasiewicz. (1998) Magic Inference Rules for Probabilistic Deduction under Taxonomic Knowledge, Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pps 354361, Madison, Wisconsin, USA, July 1998.

....[15] 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 [3, 4, 5, 10, 11, 14, 20, 21, 22, 25, 27, 29, 9] do not permit this. A few important initial attempts to incorporate different probabilistic strategies were made by Thone et al. 30] and Lakshmanan [15] which culminated in an extension of the relational algebra that accommodated different probabilistic strategies [12] In this paper, we have ....

....programs. Each of these algorithms is tuned to fit the class within which an HPP falls (i.e. class HPP 1 , HPP 2 or HPP r ; r 3) We have given algorithmic complexity analyses of these problems. To date, with the exception of the work by Kiessling s group [9, 30] and by Lukasiewicz [22], almost no work on bottom up algorithms for computing probabilistic logic programs exists. Our algorithms are the first to apply not only to HPPs, but to have finer complexity bounds for different classes of HPPs. Second, we have studied the computational complexity of the Entailment and ....

[Article contains additional citation context not shown here]

T. Lukasiewicz. (1998) Magic Inference Rules for Probabilistic Deduction under Taxonomic Knowledge, Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pps 354-361, Madison, Wisconsin, USA, July 1998.

....truth functional semantics of n valued first order logics. Our aim is to bring together the advantages of two different approaches. While the probabilistic formalism provides a well defined semantics, truth functional logics are often more compelling from the computational side (see, for example, [22] and [23] for the subtleties of probabilistic propositional deduction) In this paper, we now present a new probabilistic semantics of n valued firstorder logics that lies between the purely probabilistic semantics and the truthfunctional semantics given by the n valued Lukasiewicz logics Ln . ....

T. Lukasiewicz. Magic inference rules for probabilistic deduction under taxonomic knowledge. In Proc. of the 14th Conference on Uncertainty in Artificial Intelligence, pages 354--361. Morgan Kaufmann Publishers, 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. (1998a). Magic inference rules for probabilistic deduction under taxonomic knowledge. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 354--361. Morgan Kaufmann.

No context found.

T. Lukasiewicz, `Magic inference rules for probabilistic deduction under taxonomic knowledge', in Proc. of the 14th Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers, (1998).

....truth functional semantics of n valued first order logics. Our aim is to bring together the advantages of two different approaches. While the probabilistic formalism provides a well defined semantics, truth functional logics are often more compelling from the computational side (see, for example, [22] and [23] for the subtleties of probabilistic propositional deduction) In this paper, we now present a new probabilistic semantics of n valued firstorder logics that lies between the purely probabilistic semantics and the truthfunctional semantics given by the n valued Lukasiewicz logics Ln . ....

T. Lukasiewicz. Magic inference rules for probabilistic deduction under taxonomic knowledge. In Proc. of the 14th Conference on Uncertainty in Artificial Intelligence, pages 354--361. Morgan Kaufmann Publishers, 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. (1998a). Magic inference rules for probabilistic deduction under taxonomic knowledge. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 354--361. Morgan Kaufmann.

....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. (1998a). Magic inference rules for probabilistic deduction under taxonomic knowledge. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 354--361. Morgan Kaufmann.

....many valued logic programming in this framework is computationally more complex than classical logic programming. More precisely, some deduction problems that are P complete for classical logic programs are shown to be co NP complete for probabilistic many valued logic programs (see also [28], 29] and [30] for other work on the subtleties and the computational complexity of probabilistic deduction) We then focus on many valued logic programming in Pr n as an approximation of probabilistic many valued logic programming. Crucially, many valued logic programming in Pr n has a ....

T. Lukasiewicz. Magic inference rules for probabilistic deduction under taxonomic knowledge. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pages 354--361. Morgan Kaufmann Publishers, 1998.

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