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Algorithms
Special Issues
Logic-Based Artificial Intelligence
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Logic-Based Artificial Intelligence

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 12072

Special Issue Editor

Dr. Giovanni Amendola

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Calabria, 87036 Arcavacata, Rende, CS, Italy
Interests: artificial intelligence; knowledge representation and reasoning; logic programming; answer set programming; ontological reasoning; database theory; description logics; game theory; information extraction from the WEB; random satisfiability; quantified boolean formulas
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Logics have provided a formal basis for the study and development of systems and applications in Artificial Intelligence. In particular, logic became relevant to AI research thanks to fast satisfiability solvers for solving combinatorial search problems. Logic-based AI approaches have contributed to knowledge representation, natural language understanding, automated planning and commonsense reasoning. Nowadays, they are fundamental in the increasing request of explainability, comprehensibility and trustworthy to improve statistical-based AI systems. Hence, logic-based approaches are of paramount importance to identify promising research perspectives. The aim of this Special Issue is to collect state-of-the-art research on the topic of Logic-based AI approaches and technologies, and their main application areas.

Dr. Giovanni Amendola
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Abductive and inductive reasoning
  • Answer set programming
  • Applications of logic-based AI systems
  • Argumentation systems
  • Automated reasoning
  • Deontic logic and normative systems
  • Description logics and other logical approaches to Semantic Web and ontologies
  • Knowledge representation and reasoning
  • Logic programming and nonmonotonic reasoning
  • Logic-based data access and integration
  • Logics for uncertain and probabilistic reasoning
  • Logics in machine learning
  • Logics in multi-agent systems, games, and social choice
  • Ontology-based reasoning
  • Planning and diagnosis based on logic
  • Preferences
  • Reasoning about actions and causality

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Published Papers (4 papers)

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Editorial

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3 pages, 177 KiB  
Editorial
Special Issue on Logic-Based Artificial Intelligence
by Giovanni Amendola
Algorithms 2023, 16(2), 106; https://doi.org/10.3390/a16020106 - 13 Feb 2023
Viewed by 2402
Abstract
Since its inception, research in the field of Artificial Intelligence (AI) has had a fundamentally logical approach; therefore, discussions have taken place to establish a way of distinguishing symbolic AI from sub-symbolic AI, basing the approach instead on the statistical approaches typical of [...] Read more.
Since its inception, research in the field of Artificial Intelligence (AI) has had a fundamentally logical approach; therefore, discussions have taken place to establish a way of distinguishing symbolic AI from sub-symbolic AI, basing the approach instead on the statistical approaches typical of machine learning, deep learning or Bayesian networks [...] Full article
(This article belongs to the Special Issue Logic-Based Artificial Intelligence)

Research

Jump to: Editorial

20 pages, 342 KiB  
Article
Comparing the Reasoning Capabilities of Equilibrium Theories and Answer Set Programs
by Jorge Fandinno, David Pearce, Concepción Vidal and Stefan Woltran
Algorithms 2022, 15(6), 201; https://doi.org/10.3390/a15060201 - 8 Jun 2022
Cited by 2 | Viewed by 1969
Abstract
Answer Set Programming (ASP) is a well established logical approach in artificial intelligence that is widely used for knowledge representation and problem solving. Equilibrium logic extends answer set semantics to more general classes of programs and theories. When intertheory relations are studied in [...] Read more.
Answer Set Programming (ASP) is a well established logical approach in artificial intelligence that is widely used for knowledge representation and problem solving. Equilibrium logic extends answer set semantics to more general classes of programs and theories. When intertheory relations are studied in ASP, or in the more general form of equilibrium logic, they are usually understood in the form of comparisons of the answer sets or equilibrium models of theories or programs. This is the case for strong and uniform equivalence and their relativised and projective versions. However, there are many potential areas of application of ASP for which query answering is relevant and a comparison of programs in terms of what can be inferred from them may be important. We formulate and study some natural equivalence and entailment concepts for programs and theories that are couched in terms of inference and query answering. We show that, for the most part, these new intertheory relations coincide with their model-theoretic counterparts. We also extend some previous results on projective entailment for theories and for the new connective called fork. Full article
(This article belongs to the Special Issue Logic-Based Artificial Intelligence)
11 pages, 257 KiB  
Article
A Brief Roadmap into Uncertain Knowledge Representation via Probabilistic Description Logics
by Rafael Peñaloza
Algorithms 2021, 14(10), 280; https://doi.org/10.3390/a14100280 - 28 Sep 2021
Cited by 2 | Viewed by 2639
Abstract
Logic-based knowledge representation is one of the main building blocks of (logic-based) artificial intelligence. While most successful knowledge representation languages are based on classical logic, realistic intelligent applications need to handle uncertainty in an adequate manner. Over the years, many different languages for [...] Read more.
Logic-based knowledge representation is one of the main building blocks of (logic-based) artificial intelligence. While most successful knowledge representation languages are based on classical logic, realistic intelligent applications need to handle uncertainty in an adequate manner. Over the years, many different languages for representing uncertain knowledge—often extensions of classical knowledge representation languages—have been proposed. We briefly present some of the defining properties of these languages as they pertain to the family of probabilistic description logics. This limited view is intended to help pave the way for the interested researcher to find the most adequate language for their needs, and potentially identify the remaining gaps. Full article
(This article belongs to the Special Issue Logic-Based Artificial Intelligence)
19 pages, 1151 KiB  
Article
Fact-Checking Reasoning System for Fake Review Detection Using Answer Set Programming
by Nour Jnoub, Admir Brankovic and Wolfgang Klas
Algorithms 2021, 14(7), 190; https://doi.org/10.3390/a14070190 - 24 Jun 2021
Cited by 6 | Viewed by 3087
Abstract
A rising number of people use online reviews to choose if they want to use or buy a service or product. Therefore, approaches for identifying fake reviews are in high request. This paper proposes a hybrid rule-based fact-checking framework based on Answer Set [...] Read more.
A rising number of people use online reviews to choose if they want to use or buy a service or product. Therefore, approaches for identifying fake reviews are in high request. This paper proposes a hybrid rule-based fact-checking framework based on Answer Set Programming (ASP) and natural language processing. The paper incorporates the behavioral patterns of reviewers combined with the qualitative and quantitative properties/features extracted from the content of their reviews. As a case study, we evaluated the framework using a movie review dataset, consisting of user accounts with their associated reviews, including the review title, content, and the star rating of the movie, to identify reviews that are not trustworthy and labeled them accordingly in the output. This output is then used in the front end of a movie review platform to tag reviews as fake and show their sentiment. The evaluation of the proposed approach showed promising results and high flexibility. Full article
(This article belongs to the Special Issue Logic-Based Artificial Intelligence)
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