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Measuring the Ignorance and Degree of Satisfaction for Answering Queries in Imprecise Probabilistic Logic Programs
[chapter]
2008
Lecture Notes in Computer Science
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We first propose an approach to measuring the ignorance of a probabilistic logic program with respect to a query. ...
In probabilistic logic programming, given a query, either a probability interval or a precise probability obtained by using the maximum entropy principle is returned for the query. ...
To answer the above questions, in this paper, we propose two concepts, the measure of ignorance and the measure of the degree of satisfaction, w.r.t. a PLP and a query. ...
doi:10.1007/978-3-540-87993-0_30
fatcat:woxq6lfpqzbsxlapbwoeyvm4rq
Adaptive Dialogue Strategy Selection through Imprecise Probabilistic Query Answering
[chapter]
2011
Lecture Notes in Computer Science
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For this purpose, we use probabilistic logic programming (PLP) to model probabilistic knowledge about how these factors will affect the degree of freedom of a dialogue. ...
When a dialogue system needs to know which strategy is more suitable, an appropriate query can be executed against the PLP and a probabilistic solution with a degree of satisfaction is returned. ...
Let P and query ?Q be the same as in Example 1. Then, the degree of satisfaction for the query answer [0.7, 1] is 0.8. ...
doi:10.1007/978-3-642-22152-1_57
fatcat:tkrikb2b4jch7ituexpgn4pcli
Imprecise probabilistic query answering using measures of ignorance and degree of satisfaction
2012
Annals of Mathematics and Artificial Intelligence
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We first propose an approach to measuring the ignorance of a probabilistic logic program with respect to a query. ...
In conditional probabilistic logic programming, given a query, the two most common forms for answering the query are either a probability interval or a precise probability obtained by using the maximum ...
Acknowledgement This work is funded by the EPSRC projects with reference numbers: EP/D070864/1 and EP/D074282/1. ...
doi:10.1007/s10472-012-9286-x
fatcat:a74uvt6zazewndipyqtcr4x4am
Managing Uncertainty and Vagueness in Description Logics, Logic Programs and Description Logic Programs
[chapter]
2008
Lecture Notes in Computer Science
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Our aim is to overview basic concepts on representing uncertain and vague knowledge in current Semantic Web ontology and rule languages (and their combination). C. ...
Managing uncertainty and/or vagueness is starting to play an important role in Semantic Web representation languages. ...
Most works deal with logic programs without negation and some may provide some technique to answer queries in a top-down manner, as e.g. [35, 122, 130, 252, 276] . ...
doi:10.1007/978-3-540-85658-0_2
fatcat:6ijgfocetbfixonkur2lrnabjy
Non-commutativity and Expressive Deductive Logic Databases
[chapter]
2002
Lecture Notes in Computer Science
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The procedural semantics of multi-adjoint logic programming is used for providing a model-theoretic semantics for a data model. ...
The use of non-commutative conjunctors allows for a model of different degrees of granulation and precision, whereas expressiveness is achieved by using multiple-valued connectives. ...
The computations in the probabilistic and lattice valued fuzzy logic are the same, the only difference is handling and/or ignorance of probabilistic constraints.
Example 2. ...
doi:10.1007/3-540-45757-7_13
fatcat:5ju4spvhfbfcxeruhtrhbyy3nu
Possibility Theory and Its Applications: Where Do We Stand?
[chapter]
2015
Springer Handbook of Computational Intelligence
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Possibilistic logic provides a rich representation setting, which enables the handling of lower bounds of possibility theory measures, while remaining close to classical logic. ...
Shackle also introduces a notion of conditional possibility, whereby the degree of surprise of a conjunction of two events A and B is equal to the maximum of the degree of surprise of A, and of the degree ...
event expressing the gradual satisfaction of the query [51] . ...
doi:10.1007/978-3-662-43505-2_3
fatcat:qptvfae6hndopkvagiwlsnxaxu
Query Containment of Tier-2 Queries over a Probabilistic Database
2009
International VLDB workshop on Management of Uncertain Data
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The second tutorial is followed by a discussion on the differences and commonalities between probabilistic and possibilistic databases. We would like to thank the reviewers for their time and effort. ...
We would also like to thank the Centre Telematics and Information Technology for sponsoring the proceedings of the workshop. ...
We would like to thank Hyunjung Park for several helpful discussions.
Acknowledgements This work was partially funded by NSF IIS-0713576 and the eScience Institute at the University of Washington. ...
dblp:conf/mud/MooreRRS09
fatcat:k25665bptbcafkpbvke5a7egta
Representation and Access of Uncertain Relational Data
1989
IEEE Data Engineering Bulletin
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In this paper we are interested in both the handling of flexible requests and the management of data pervaded by imprecision, uncertainty or vagueness. ...
A degree of matching reflects our lack of certainty that the item of data satisfies the request, and may be due either to the fact that the available information in the data is insufficient or to the fact ...
ACKNOWLEDGMENT The implementation of FIIS was carried out by Suresh Rajgopal and Richard Roland. Acknowledgement: Thanks to Ami Motro for many suggestions which improved this presentation. ...
dblp:journals/debu/Tzvieli89
fatcat:zqhzab6apbf7lohhbvszrjyzzu
Managing uncertainty and vagueness in description logics for the Semantic Web
2008
Journal of Web Semantics
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In this paper, we give an overview of approaches in this context to managing probabilistic uncertainty, possibilistic uncertainty, and vagueness in expressive description logics for the Semantic Web. ...
Note that the maximal speed limit of the mgb car ( 170km/h ) induces the upper limit 0.72 of the membership degree. ...
This work has been partially supported by the German Research Foundation (DFG) under the Heisenberg Programme. We thank the review- ...
doi:10.1016/j.websem.2008.04.001
fatcat:te5ii2ore5egdfrrq4p2xrrzbi
Managing Uncertainty and Vagueness in Description Logics for the Semantic Web
2008
Social Science Research Network
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2011; you can also visit the original URL.
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In this paper, we give an overview of approaches in this context to managing probabilistic uncertainty, possibilistic uncertainty, and vagueness in expressive description logics for the Semantic Web. ...
Note that the maximal speed limit of the mgb car ( 170km/h ) induces the upper limit 0.72 of the membership degree. ...
This work has been partially supported by the German Research Foundation (DFG) under the Heisenberg Programme. We thank the review- ...
doi:10.2139/ssrn.3199411
fatcat:j3o6unkvafc65eurwpsea33t3u
Probabilistic XML: Models and Complexity
[chapter]
2013
Studies in Fuzziness and Soft Computing
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Various models of probabilistic XML provide different languages, with various degrees of expressiveness, for such compact representations. ...
For instance, query evaluation entails probabilistic inference, and update operations need to properly change the entire probability space. ...
We are grateful to Evgeny Kharlamov for his helpful comments. This work was partially supported by the European Research Council grant Webdam (under FP7), grant agreement 226513. ...
doi:10.1007/978-3-642-37509-5_3
fatcat:lcoqkx7euvfb3dietqvb6v3qva
A survey of formalisms for representing and reasoning with scientific knowledge
2010
Knowledge engineering review (Print)
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Some KR formalisms have been applied to capturing scientific knowledge, in particular description logics, logic programming, argumentation systems, and uncertainty formalisms. ...
Using such formalisms, we can undertake reasoning with the uncertainty and inconsistency to allow automated techniques to be used for querying and combining of scientific knowledge. ...
The usefulness of probabilistic logic programs to represent imprecise probabilistic knowledge and harness this knowledge to answer queries can further be demonstrated by an example from biochemistry on ...
doi:10.1017/s0269888910000019
fatcat:ijdmd5gnobhxdjka2rjrrkztcq
Constraint-based optimization and utility elicitation using the minimax decision criterion
2006
Artificial Intelligence
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But the bound is tight if maxspan is defined to account for logical consistency. 19 Even termination can be determined heuristically, for example, by computing the max regret of the optimistic state after ...
First, we propose the use of minimax regret as a suitable decision criterion for decision making in the presence of such utility function uncertainty. ...
Acknowledgements This research was supported by the Institute for Robotics and Intelligent Systems (IRIS) and the Natural Sciences and Engineering Research Council (NSERC). ...
doi:10.1016/j.artint.2006.02.003
fatcat:73sqdofybbafxbkusss23i2u6a
Situation Theory and Channel theory as a Unified Framework for Imperfect Information Management
[article]
2022
arXiv
pre-print
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Among many models of them, possibility theory and probabilistic logic theory are the best approaches. ...
The objectification process in these theories reveals to us the nature of default or probabilistic rules in perceptions. ...
The quantity of Ign(A) = Pl(A)-Bel(A) shows the degree of ignorance about A. ...
arXiv:2206.02283v1
fatcat:7yyukyfe4zbfxjbupid4o5257u
PrASP Report
[article]
2016
arXiv
pre-print
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This technical report describes the usage, syntax, semantics and core algorithms of the probabilistic inductive logic programming framework PrASP. ...
In particular, PrASP allows for ASP as well as First-Order Logic syntax, and for the annotation of formulas with point probabilities as well as interval probabilities. ...
However, the semantics for the non-probabilistic part of our logic is always the stable model semantics used in Answer Set Programming -first-order formulas are internally transformed into equivalent ASP ...
arXiv:1612.09591v1
fatcat:6znpsfmqs5gw5jnhjgdcxi2qny
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