11,140 research outputs found
Bayesian Logic Programs
Bayesian networks provide an elegant formalism for representing and reasoning
about uncertainty using probability theory. Theyare a probabilistic extension
of propositional logic and, hence, inherit some of the limitations of
propositional logic, such as the difficulties to represent objects and
relations. We introduce a generalization of Bayesian networks, called Bayesian
logic programs, to overcome these limitations. In order to represent objects
and relations it combines Bayesian networks with definite clause logic by
establishing a one-to-one mapping between ground atoms and random variables. We
show that Bayesian logic programs combine the advantages of both definite
clause logic and Bayesian networks. This includes the separation of
quantitative and qualitative aspects of the model. Furthermore, Bayesian logic
programs generalize both Bayesian networks as well as logic programs. So, many
ideas developedComment: 52 page
Logic-Based Decision Support for Strategic Environmental Assessment
Strategic Environmental Assessment is a procedure aimed at introducing
systematic assessment of the environmental effects of plans and programs. This
procedure is based on the so-called coaxial matrices that define dependencies
between plan activities (infrastructures, plants, resource extractions,
buildings, etc.) and positive and negative environmental impacts, and
dependencies between these impacts and environmental receptors. Up to now, this
procedure is manually implemented by environmental experts for checking the
environmental effects of a given plan or program, but it is never applied
during the plan/program construction. A decision support system, based on a
clear logic semantics, would be an invaluable tool not only in assessing a
single, already defined plan, but also during the planning process in order to
produce an optimized, environmentally assessed plan and to study possible
alternative scenarios. We propose two logic-based approaches to the problem,
one based on Constraint Logic Programming and one on Probabilistic Logic
Programming that could be, in the future, conveniently merged to exploit the
advantages of both. We test the proposed approaches on a real energy plan and
we discuss their limitations and advantages.Comment: 17 pages, 1 figure, 26th Int'l. Conference on Logic Programming
(ICLP'10
Storing and Querying Probabilistic XML Using a Probabilistic Relational DBMS
This work explores the feasibility of storing and querying probabilistic XML in a probabilistic relational database. Our approach is to adapt known techniques for mapping XML to relational data such that the possible worlds are preserved. We show that this approach can work for any XML-to-relational technique by adapting a representative schema-based (inlining) as well as a representative schemaless technique (XPath Accelerator). We investigate the maturity of probabilistic rela- tional databases for this task with experiments with one of the state-of- the-art systems, called Trio
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