Example of Theory and Practice of Logic Programming format
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Example of Theory and Practice of Logic Programming format Example of Theory and Practice of Logic Programming format Example of Theory and Practice of Logic Programming format Example of Theory and Practice of Logic Programming format Example of Theory and Practice of Logic Programming format Example of Theory and Practice of Logic Programming format
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Example of Theory and Practice of Logic Programming format Example of Theory and Practice of Logic Programming format Example of Theory and Practice of Logic Programming format Example of Theory and Practice of Logic Programming format Example of Theory and Practice of Logic Programming format Example of Theory and Practice of Logic Programming format
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open access Open Access

Theory and Practice of Logic Programming — Template for authors

Categories Rank Trend in last 3 yrs
Computational Theory and Mathematics #42 of 133 down down by 18 ranks
Theoretical Computer Science #43 of 120 down down by 18 ranks
Artificial Intelligence #106 of 227 down down by 43 ranks
Hardware and Architecture #77 of 157 down down by 37 ranks
Software #191 of 389 down down by 70 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 156 Published Papers | 546 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 04/07/2020
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Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

1.076

3% from 2018

Impact factor for Theory and Practice of Logic Programming from 2016 - 2019
Year Value
2019 1.076
2018 1.111
2017 0.875
2016 1.38
graph view Graph view
table view Table view

3.5

13% from 2019

CiteRatio for Theory and Practice of Logic Programming from 2016 - 2020
Year Value
2020 3.5
2019 3.1
2018 3.7
2017 3.9
2016 3.6
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 3% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 13% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

0.685

14% from 2019

SJR for Theory and Practice of Logic Programming from 2016 - 2020
Year Value
2020 0.685
2019 0.798
2018 0.658
2017 0.524
2016 0.86
graph view Graph view
table view Table view

1.383

18% from 2019

SNIP for Theory and Practice of Logic Programming from 2016 - 2020
Year Value
2020 1.383
2019 1.685
2018 1.542
2017 1.534
2016 1.776
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 14% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 18% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Theory and Practice of Logic Programming

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Cambridge University Press

Theory and Practice of Logic Programming

Theory and Practice of Logic Programming emphasises both the theory and practice of logic programming. Logic programming applies to all areas of artificial intelligence and computer science and is fundamental to all of them. Among the topics covered are AI applications that us...... Read More

Computer Science

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Last updated on
04 Jul 2020
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ISSN
1471-0684
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Impact Factor
High - 1.925
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Open Access
No
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Sherpa RoMEO Archiving Policy
Green faq
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Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
unsrt
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Citation Type
Numbered
[25]
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Bibliography Example
G E Blonder, M Tinkham, and T M Klapwijk. Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys. Rev. B, 25(7):4515–4532, 1982. 10.1103/PhysRevB.25.4515.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1017/S1471068403001674
Defeasible logic programming: an argumentative approach

Abstract:

The work reported here introduces Defeasible Logic Programming (DeLP), a formalism that combines results of Logic Programming and Defeasible Argumentation. DeLP provides the possibility of representing information in the form of weak rules in a declarative manner, and a defeasible argumentation inference mechanism for warrant... The work reported here introduces Defeasible Logic Programming (DeLP), a formalism that combines results of Logic Programming and Defeasible Argumentation. DeLP provides the possibility of representing information in the form of weak rules in a declarative manner, and a defeasible argumentation inference mechanism for warranting the entailed conclusions. In DeLP an argumentation formalism will be used for deciding between contradictory goals. Queries will be supported by arguments that could be defeated by other arguments. A query $q$ will succeed when there is an argument ${\mathcal A}$ for $q$ that is warranted, i.e. the argument ${\mathcal A}$ that supports $q$ is found undefeated by a warrant procedure that implements a dialectical analysis. The defeasible argumentation basis of DeLP allows to build applications that deal with incomplete and contradictory information in dynamic domains. Thus, the resulting approach is suitable for representing agent's knowledge and for providing an argumentation based reasoning mechanism to agents. read more read less

Topics:

Defeasible logic (70%)70% related to the paper, Argumentation framework (64%)64% related to the paper, Argumentation theory (55%)55% related to the paper, Logic programming (54%)54% related to the paper
View PDF
878 Citations
open accessOpen access Journal Article DOI: 10.1017/S1471068414000076
Inference and learning in probabilistic logic programs using weighted Boolean formulas

Abstract:

Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for probabilistic logic programs. Several such tasks such as computing the marginals given evid... Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for probabilistic logic programs. Several such tasks such as computing the marginals given evidence and learning from (partial) interpretations have not really been addressed for probabilistic logic programs before. The rst contribution of this paper is a suite of ecient algorithms for various inference tasks. It is based on a conversion of the program and the queries and evidence to a weighted Boolean formula. This allows us to reduce the inference tasks to well-studied tasks such as weighted model counting, which can be solved using state-of-the-art methods known from the graphical model and knowledge compilation literature. The second contribution is an algorithm for parameter estimation in the learning from interpretations setting. The algorithm employs Expectation Maximization, and is built on top of the developed inference algorithms. The proposed approach is experimentally evaluated. The results show that the inference algorithms improve upon the state-of-the-art in probabilistic logic programming and that it is indeed possible to learn the parameters of a probabilistic logic program from interpretations. read more read less

Topics:

Probabilistic logic network (70%)70% related to the paper, Probabilistic CTL (67%)67% related to the paper, Probabilistic logic (66%)66% related to the paper, Probabilistic argumentation (64%)64% related to the paper, Inference (64%)64% related to the paper
View PDF
284 Citations
Journal Article DOI: 10.1017/S1471068408003645
Probabilistic reasoning with answer sets
Chitta Baral1, Michael Gelfond2, J. Nelson Rushton2

Abstract:

This paper develops a declarative language, P-log, that combines logical and probabilistic arguments in its reasoning. Answer Set Prolog is used as the logical foundation, while causal Bayes nets serve as a probabilistic foundation. We give several non-trivial examples and illustrate the use of P-log for knowledge representat... This paper develops a declarative language, P-log, that combines logical and probabilistic arguments in its reasoning. Answer Set Prolog is used as the logical foundation, while causal Bayes nets serve as a probabilistic foundation. We give several non-trivial examples and illustrate the use of P-log for knowledge representation and updating of knowledge. We argue that our approach to updates is more appealing than existing approaches. We give sufficiency conditions for the coherency of P-log programs and show that Bayes nets can be easily mapped to coherent P-log programs. read more read less

Topics:

Reasoning system (60%)60% related to the paper, Probabilistic argumentation (59%)59% related to the paper, Knowledge representation and reasoning (59%)59% related to the paper, Probabilistic logic (58%)58% related to the paper, Stable model semantics (56%)56% related to the paper
View PDF
281 Citations
open accessOpen access Journal Article DOI: 10.1017/S1471068407003213
N3logic: A logical framework for the world wide web
Tim Berners-Lee1, Dan Connolly1, Lalana Kagal1, Yosi Scharf1, James A. Hendler2

Abstract:

The Semantic Web drives toward the use of the Web for interacting with logically interconnected data. Through knowledge models such as Resource Description Framework (RDF), the Semantic Web provides a unifying representation of richly structured data. Adding logic to the Web implies the use of rules to make inferences, choose... The Semantic Web drives toward the use of the Web for interacting with logically interconnected data. Through knowledge models such as Resource Description Framework (RDF), the Semantic Web provides a unifying representation of richly structured data. Adding logic to the Web implies the use of rules to make inferences, choose courses of action, and answer questions. This logic must be powerful enough to describe complex properties of objects but not so powerful that agents can be tricked by being asked to consider a paradox. The Web has several characteristics that can lead to problems when existing logics are used, in particular, the inconsistencies that inevitably arise due to the openness of the Web, where anyone can assert anything. N3Logic is a logic that allows rules to be expressed in a Web environment. It extends RDF with syntax for nested graphs and quantified variables and with predicates for implication and accessing resources on the Web, and functions including cryptographic, string, math. The main goal of N3Logic is to be a minimal extension to the RDF data model such that the same language can be used for logic and data. In this paper, we describe N3Logic and illustrate through examples why it is an appropriate logic for the Web. read more read less

Topics:

Web modeling (71%)71% related to the paper, Social Semantic Web (70%)70% related to the paper, Semantic Web (69%)69% related to the paper, Web standards (68%)68% related to the paper, Semantic Web Rule Language (68%)68% related to the paper
View PDF
220 Citations
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13. What is Sherpa RoMEO Archiving Policy for Theory and Practice of Logic Programming?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Theory and Practice of Logic Programming. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Theory and Practice of Logic Programming?

The 5 most common citation types in order of usage for Theory and Practice of Logic Programming are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

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