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MAP Inference for Probabilistic Logic Programming
[article]
2020
arXiv
pre-print
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In Probabilistic Logic Programming (PLP) the most commonly studied inference task is to compute the marginal probability of a query given a program. ...
In this paper, we consider two other important tasks in the PLP setting: the Maximum-A-Posteriori (MAP) inference task, which determines the most likely values for a subset of the random variables given ...
References Alberti, M., Bellodi, E., Cota, G., Riguzzi, F., and Zese, R. 2017. cplint on SWISH: Probabilistic logical inference with a web browser. Intell. Artif. 11, 1, 47-64. ...
arXiv:2008.01394v3
fatcat:m6xaz5lzjjfi7icisgd6yjajfm
ADDING PROBABILITIES AND RULES TO OWL LITE SUBSETS BASED ON PROBABILISTIC DATALOG
2006
International Journal of Uncertainty Fuzziness and Knowledge-Based Systems
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As inference engines for probabilistic Datalog are available, description logics and information retrieval systems can easily be combined. ...
program. ...
We wish to thank Umberto Straccia for fruitful discussions. The constructive comments of the reviewers helped improving the paper significantly. ...
doi:10.1142/s0218488506003819
fatcat:otvi4idllvg4zidvjjkkhlyuoi
Uncertainty Reasoning for the Semantic Web
[chapter]
2017
Lecture Notes in Computer Science
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Tightly integrated probabilistic description logic programs for representing ontology mappings. Submitted for journal publication, March 2009. ...
probabilistic inference based on lexicographic entailment in probabilistic logic (for combining assertional and terminological probabilistic knowledge) terminological and assertional probabilistic inference ...
doi:10.1007/978-3-319-61033-7_8
fatcat:hszvhxxiind3pjxrw6d6rnmmse
Uncertainty Reasoning for the Semantic Web
[chapter]
2009
Lecture Notes in Computer Science
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Tightly integrated probabilistic description logic programs for representing ontology mappings. Submitted for journal publication, March 2009. ...
probabilistic inference based on lexicographic entailment in probabilistic logic (for combining assertional and terminological probabilistic knowledge) terminological and assertional probabilistic inference ...
doi:10.1007/978-3-642-05082-4_3
fatcat:ojaedt2s6jc2jmocn2ie2ewejy
Complex Coordinate-Based Meta-Analysis with Probabilistic Programming
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2012.01303v1 fatcat:nsjtdb2upnbqdlgyp5teq6vdfu
We design a probabilistic domain-specific language (DSL) standing on Datalog and one of its probabilistic extensions, CP-Logic, for expressing and solving rich logic-based queries. ...
We encode a CBMA database into a probabilistic program. ...
Firstly, we investigate the feasibility and technicalities of applying probabilistic logic programming to CBMA-based brain mapping. ...
arXiv:2012.01303v2
fatcat:o3prgvoo6vgz3kge4w7egd5epi
Paraconsistent Foundations for Probabilistic Reasoning, Programming and Concept Formation
[article]
2021
arXiv
pre-print
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Then variations on the Curry-Howard correspondence are used to map these paraconsistent and probabilistic logics into probabilistic types suitable for use within dependent type based programming languages ...
It is argued that 4-valued paraconsistent truth values (called here "p-bits") can serve as a conceptual, mathematical and practical foundation for highly AI-relevant forms of probabilistic logic and probabilistic ...
for a number of interrelated, critical AI ideas including PLN probabilistic logic and variations of probabilistic programming. ...
arXiv:2012.14474v2
fatcat:4zfsz3taqnbfjayux7zly7w2ia
The Complexity of Inferences and Explanations in Probabilistic Logic Programming
[chapter]
2017
Lecture Notes in Computer Science
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A popular family of probabilistic logic programming languages combines logic programs with independent probabilistic facts. ...
We study the complexity of marginal inference, most probable explanations, and maximum a posteriori calculations for propositional/relational probabilistic logic programs that are acyclic/definite/stratified ...
Introduction The goal of this paper is to shed light on the computational complexity of inference for probabilistic logic programs interpreted in the spirit of Sato's distribution semantics [25] ; that ...
doi:10.1007/978-3-319-61581-3_40
fatcat:vjwhfzvzb5hlncn3vs4lyytxbi
SMProbLog: Stable Model Semantics in ProbLog and its Applications in Argumentation
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2110.01990v1 fatcat:ztq3nerscrhsplem7b33wt2fpu
Therefore, the key contribution of this paper are: a more general semantics for ProbLog programs, its implementation into a probabilistic programming framework for both inference and parameter learning ...
We introduce SMProbLog, a generalization of the probabilistic logic programming language ProbLog. ...
At the same time, we propose a mapping from probabilistic argument graphs to probabilistic logic programs, which provides a novel semantics for epistemic argument graphs. ...
arXiv:2110.01990v2
fatcat:luax2uanmvcpvmdqhyrfrwvxce
From Statistical Relational to Neural Symbolic Artificial Intelligence: a Survey
[article]
2022
arXiv
pre-print
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Neural-symbolic and statistical relational artificial intelligence both integrate frameworks for learning with logical reasoning. ...
These cannot only be used to characterize and position neural-symbolic artificial intelligence approaches but also to identify a number of directions for further research. ...
This program can be mapped to the Bayesian network in Figure 2 This probabilistic logic program defines a distribution p over possible worlds ω. ...
arXiv:2108.11451v2
fatcat:2vynob3s7bhsjk22pwv5e5hnta
Probabilistic (logic) programming concepts
2015
Machine Learning
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While doing so, we focus on probabilistic extensions of logic programming languages such as Prolog, which have been considered for over 20 years. ...
Each of these languages employs its own probabilistic primitives, and comes with a particular syntax, semantics and inference procedure. ...
Acknowledgements The authors are indebted to Bernd Gutmann and Ingo Thon for participating in many discussions, and contributing several ideas during the early stages of the research that finally led to ...
doi:10.1007/s10994-015-5494-z
fatcat:6bgcvas4lfed5invj2jjb7wfmy
Systems AI: A Declarative Learning Based Programming Perspective
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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We classify the existing frameworks based on the type of techniques as well as the data and knowledge representations they use, provide a comparative study of the way they address the challenges of programming ...
We review key efforts made by various AI communities to provide languages for high-level abstractions over learning and reasoning techniques needed for designing complex AI systems. ...
LBJava, RELOOP and Saul map the inference problems under the domain's logical constraints to form integer linear programs and use efficient off the shelf techniques in that area to solve inference. ...
doi:10.24963/ijcai.2018/771
dblp:conf/ijcai/KordjamshidiRK18
fatcat:tpwajnwywrfkpm3dcc3553oxca
From Statistical Relational to Neuro-Symbolic Artificial Intelligence
[article]
2020
arXiv
pre-print
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Neuro-symbolic and statistical relational artificial intelligence both integrate frameworks for learning with logical reasoning. ...
These cannot only be used to characterize and position neuro-symbolic artificial intelligence approaches but also to identify a number of directions for further research. ...
One such idea concerns performing probabilistic inference by mapping it onto a weighted model counting (WMC) problem. ...
arXiv:2003.08316v2
fatcat:tlgua7bvyvbftcnlngn7drix34
Scaling-Up MAP and Marginal MAP Inference in Markov Logic
2016
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Markov Logic Networks (MLNs) use a few weighted first-order logic formulas to represent large probabilistic graphical models and are ideally suited for representing both relational and probabilistic knowledge ...
The aim of the proposed thesis is to fill this void, by developing next generation inference algorithms for MAP and marginal MAP inference. ...
Introduction Markov Logic Networks (Domingos and Lowd 2009) (MLNs) use a few weighted first-order logic formulas to represent large probabilistic graphical models and are ideally suited for representing ...
doi:10.1609/aaai.v30i1.9820
fatcat:aal3qnecqnfqlpvhhvahps25py
Cumulative subject index volumes 68–71
1986
Information and Control
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, probabilistic analysis, 68, 47 optimally data-efficient, for isomorphism inference: ...
Aggregation problems, formulation of several versions, analysis from recursion theoretic point of view, 70, 69 Algorithms for circular retrieval and k-nearest neighbor search problems, description and ...
temporal logics, probabilistic propositional
temporal logic based on, description
of two versions, 70, 97
C
Calculus
lambda
and categorical combinatory logic, syn-
tactic equivalence theorem, ...
doi:10.1016/s0019-9958(86)80014-6
fatcat:hkligygazvcyvdgrhtvhrefpj4
UTexas: Natural Language Semantics using Distributional Semantics and Probabilistic Logic
2014
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)
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We use Markov Logic Networks (MLN) for the RTE task, and Probabilistic Soft Logic (PSL) for the STS task. The system is evaluated on the SICK dataset. ...
We represent natural language semantics by combining logical and distributional information in probabilistic logic. ...
Acknowledgements This research was supported by the DARPA DEFT program under AFRL grant FA8750-13-2-0026. Some experiments were run on the Mastodon Cluster supported by NSF Grant EIA-0303609. ...
doi:10.3115/v1/s14-2141
dblp:conf/semeval/BeltagyRBEM14
fatcat:jd37wdsglfdhxgqyqzbrzx3mwy
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