Lukasiewicz T.: Efficient Global Probabilistic Deduction from Taxonomic and Probabilistic Knowledge-Bases over Conjunctive Events. Proc. of the 6th International Conference on Information and Knowledge Management, ACM Press, 75-82 (1997)  Home/Search   Document Details and Download   Summary   Related Articles   Check

....[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 ....

.... To our knowledge, this paper is the first paper to contain a detailed analysis of complexity results in probabilistic logic programs, though [12] contains some results for probabilistic relational algebra, and [14] contains some results for a different probabilistic framework, and Lukasiewicz[20, 21, 22, 23] proves some elegant complexity results for a mix of multivalued and probabilistic logic programming. Finally, we have described a proof system for HPPs that guarantees that for every F : that is a ground logical consequence of an HPP P , we have a polynomially bounded proof of F : which in ....

T. Lukasiewicz. (1997) Efficient Global Probabilistic Deduction from Taxonomic and Probabilistic Knowledge-Bases over Conjunctive Events, Proc. Conf. on Information and Knowledge Management, pps 75--82.

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Lukasiewicz, T.: 1997, `Efficient global probabilistic deduction from taxonomic and probabilistic knowledge-bases over conjunctive events'. In: Proceedings of the 6th International Conference on Information and Knowledge Management. pp. 75--82.

....al. 26] Other techniques, which may be described as problem transformations on the language level, have been successfully applied in probabilistic logic programming [28] Moreover, a global approach for the conjunctive case, which characterizes a reduced set of variables, has been presented in [29]. We point out that in model theoretic probabilistic logic, for every conditional constraint in the given probabilistic knowledge base, the conditional probability is looked at as the ratio of , so that it is defined only if . On the contrary, as well known, within the approach of de ....

....[21, 27, 26] # We describe two other techniques for the conjunctive case, namely (i) removing inactive logical and conditional constraints, and (ii) producing a reduced set of variables for the linear optimization problems. The former is inspired by [28, 16] while the latter is taken from [29]. # Interestingly, it turns out that the technique of generating a reduced set of variables taken from [29] can be characterized using the notion of random gain. The rest of this paper is organized as follows. Section 2 introduces the formal background of this work, and recalls the semantic ....

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T. Lukasiewicz. Efficient global probabilistic deduction from taxonomic and probabilistic knowledge-bases over conjunctive events. In Proceedings CIKM-97, pages 75-- 82. ACM Press, 1997.

No context found.

T. Lukasiewicz. Efficient global probabilistic deduction from taxonomic and probabilistic knowledge-bases over conjunctive events. In Proceedings CIKM-97, pages 75--82. ACM Press, 1997.

....al. 26] Other techniques, which may be described as problem transformations on the language level, have been successfully applied in probabilistic logic programming [28] Moreover, a global approach for the conjunctive case, which characterizes a reduced set of variables, has been presented in [29]. We point out that in model theoretic probabilistic logic, for every conditional constraint P ( j ) in the given probabilistic knowledge base, the conditional probability P ( j ) is looked at as the ratio of P ( and P ( so that it is defined only if P ( 0. On the contrary, as ....

....[21, 27, 26] We describe two other techniques for the conjunctive case, namely (i) removing inactive logical and conditional constraints, and (ii) producing a reduced set of variables for the linear optimization problems. The former is inspired by [28, 16] while the latter is taken from [29]. Interestingly, it turns out that the technique of generating a reduced set of variables taken from [29] can be characterized using the notion of random gain. The rest of this paper is organized as follows. Section 2 introduces the formal background of this work, and recalls the semantic ....

[Article contains additional citation context not shown here]

T. Lukasiewicz. Efficient global probabilistic deduction from taxonomic and probabilistic knowledge-bases over conjunctive events. In Proceedings CIKM-97, pages 75-- 82. ACM Press, 1997.

....al. 25] Other techniques, which may be described as problem transformations on the language level, have been successfully applied in probabilistic logic programming [27] Moreover, a global approach for the conjunctive case, which characterizes a reduced set of variables, has been presented in [28]. We point out that in model theoretic probabilistic logic, for every conditional constraint P ( j ) in the given probabilistic knowledge base, the conditional probability P ( j ) is looked at as the ratio of P ( and P ( so that it is defined only if P ( 0. On the contrary, as ....

....[20, 26, 25] We describe two other techniques for the conjunctive case, namely (i) removing inactive logical and conditional constraints, and (ii) producing a reduced set of variables for the linear optimization problems. The former is inspired by [27, 15] while the latter is taken from [28]. Interestingly, it turns out that the technique of generating a reduced set of variables taken from [28] can be characterized using the notion of random gain. The rest of this paper is organized as follows. Section 2 introduces the formal background of this work, and recalls the semantic ....

[Article contains additional citation context not shown here]

T. Lukasiewicz. Efficient global probabilistic deduction from taxonomic and probabilistic knowledge-bases over conjunctive events. In Proceedings CIKM-97, pages 75--82. ACM Press, 1997.

....Finally, the intuition behind R5 is that we actually do not need the fine granularity of the set of relevant possible worlds. It is sufficient to work with equivalence classes of possible worlds. Note that these equivalence classes are a generalization of the equivalence classes presented in [41] for conjunctive events. They are based on the concept of conditional events (see especially [12] and [6] 6.2.2 Definitions We start with some preparative definitions. Let L be a classical knowledge base and let F be a probabilistic knowledge base. We first define the operator S F , which ....

T. Lukasiewicz. Efficient global probabilistic deduction from taxonomic and probabilistic knowledge-bases over conjunctive events. In Proceedings of the 6th International Conference on Information and Knowledge Management, pages 75--82. ACM Press, 1997.

....programs are already NP hard for probabilistic logic programs. Hence, any attempt towards efficient deduction in probabilistic logic programs should be guided by looking for efficient special case, average case, or approximation techniques. As a third contribution, by generalizing own work from [18], we elaborate a linear programming approach to deduction in probabilistic logic programs, which is efficient in interesting special cases. In our framework, probabilistic deduction problems can easily be represented by linear programs. However, these initial linear programs have a number of ....

....subset of HBP[f g as follows. If C 6= then R(C) is the set of all ground atomic formulas that occur in C. If C = then R(C) is HBP [ f g. We can now specify a reduced set of possible worlds (this reduction is correct with respect to computing the requested tight answer, as we prove in [18]) the reduced set of possible worlds W red P WP [ fHBP [ f gg is the least set with R(CP ) W red P and with W1 [ W2 2 W red P if W1 ; W2 2 W red P and W 2 R(CP ) such that W1 ; W2 oe W and W is minimal in R(CP ) with respect to set inclusion. In our example, we have W red P = R(CP ....

[Article contains additional citation context not shown here]

T. Lukasiewicz, `Efficient global probabilistic deduction from taxonomic and probabilistic knowledge-bases over conjunctive events', in Proc. of the 6th International Conference on Information and Knowledge Management, pp. 75--82. ACM Press, (1997).

....upper bound. This kind of probabilistic deduction problems can be solved in a global approach by linear programming or in a local approach by the iterative application of inference rules. The global approach by linear programming (see, for example, 21] 12] 22] 10] 15] 14] 3] and [18]) can be performed within rich probabilistic languages capable of representing many facets of probabilistic knowledge (see especially [10] Probabilistic deduction by linear programming is globally complete, that is, it really produces the requested tightest bounds entailed by the whole ....

T. Lukasiewicz. Efficient global probabilistic deduction from taxonomic and probabilistic knowledge-bases over conjunctive events. In Proc. of the 6th International Conference on Information and Knowledge Management, pages 75--82. ACM Press, 1997.

....about whether to solve this kind of probabilistic deduction problems in a global approach by linear programming or in a local approach by the iterative application of inference rules. The global approach by linear programming (see, for example, 23] 13] 10] 17] 16] 3] 22] and [20]) can be performed within rich probabilistic languages capable of representing many facets of probabilistic knowledge (see especially [10] Crucially, probabilistic deduction by linear programming is globally complete, that is, it really produces the requested tightest bounds entailed by the ....

T. Lukasiewicz. Efficient global probabilistic deduction from taxonomic and probabilistic knowledgebases over conjunctive events. In Proc. of the 6 th International Conference on Information and Knowledge Management, pages 75--82. ACM Press, 1997.

No context found.

Lukasiewicz T.: Efficient Global Probabilistic Deduction from Taxonomic and Probabilistic Knowledge-Bases over Conjunctive Events. Proc. of the 6th International Conference on Information and Knowledge Management, ACM Press, 75-82 (1997)

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