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Knowledge Representation in a Proof Checker for Logic Programs [chapter]

Emmanouil Marakakis, Haridimos Kondylakis, Nikos Papadakis
2012 Advances in Knowledge Representation  
Knowledge Representation in a Proof Checker for Logic Programs 163 specification expressed in typed FOL into structured form which is required by our correctness method (Marakakis, 1997) , (Marakakis,  ...  Knowledge is a description of the world, i.e. the problem domain. Representation is how knowledge is encoded.  ...  Declarative knowledge concerns representation of the problem domain (world) as a set of truth sentences. This representation expresses "what something is".  ... 
doi:10.5772/37201 fatcat:5hu5b4ytlbcuvaqg6o7233svti

Exposing the Causal Structure of Processes by Learning CP-Logic Programs [chapter]

Hendrik Blockeel
2008 Lecture Notes in Computer Science  
It is a knowledge representation formalism that allows us to write down rules that indicate that a certain combination of conditions may cause certain effects with a particular probability (e.g., tossing  ...  This talk starts with a brief introduction to probabilistic logic learning, after which we will focus on a relatively new formalism known as CP-logic. CPlogic stands for "causal probabilistic logic".  ...  It is a knowledge representation formalism that allows us to write down rules that indicate that a certain combination of conditions may cause certain effects with a particular probability (e.g., tossing  ... 
doi:10.1007/978-3-540-89197-0_2 fatcat:bcih2g3zt5djloxu7p2uzuwkqm

Special issue: abductive logic programming

Marc Denecker, Antonis Kakas
2000 The Journal of Logic Programming  
Declarative problem solving attempts to tackle problems using a high-level representation of the expert knowledge on the problem at hand.  ...  In a truly declarative representation of the problem, the logic theory contains knowledge known to be true about the problem domain rather than information on how to solve tasks, i.e. it does not contain  ...  In the context of ALP, an expert represents his or her strong de®nitional knowledge, i.e. knowledge which fully determines one or a group of predicates in terms of other predicates, as a set of logic programming  ... 
doi:10.1016/s0743-1066(99)00078-3 fatcat:eazj3ukecrdephhdclbfaewwqe

Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning [article]

Artur d'Avila Garcez, Marco Gori, Luis C. Lamb, Luciano Serafini, Michael Spranger, Son N. Tran
2019 arXiv   pre-print
In this paper, we survey recent accomplishments of neural-symbolic computing as a principled methodology for integrated machine learning and reasoning.  ...  We illustrate the effectiveness of the approach by outlining the main characteristics of the methodology: principled integration of neural learning with symbolic knowledge representation and reasoning  ...  This result allowed applications of reasoning and learning using backpropagation and logic programs as background knowledge [17] .  ... 
arXiv:1905.06088v1 fatcat:gm4f3ncukrbevpd7nq5yr75ar4

On the Use of Multi-dimensional Dynamic Logic Programming to Represent Societal Agents' Viewpoints [chapter]

João Alexandre Leite, José Júlio Alferes, Luís Moniz Pereira
2001 Lecture Notes in Computer Science  
of Logic Programming, hat allows the combination of several non-monotonic knowledge representation and reasoning mechanisms developed in recent years.  ...  This paper explores the applicability of the new paradigm of Multi-dimensional Dynamic Logic Programming to represent an agent's view of the combination of societal knowledge dynamics.  ...  Since logic programs describe knowledge states, it's only fit that logic programs describe transitions of knowledge states as well.  ... 
doi:10.1007/3-540-45329-6_28 fatcat:pf4aa3cttfg2reneq5fwz2a5ny

Inductive logic programming applied for knowledge representation in computer music/ Programação lógica indutiva aplicada para representação do conhecimento em música computacional

Clenio B. Gonçalves Junior, Murillo Rodrigo Petrucelli Homem
2021 Brazilian Applied Science Review  
The present work performs a systematic review based on approaches that use Inductive Logic Programming in the representation of musical knowledge.  ...  In this sense, Inductive Logic Programming presents itself as a research field that incorporates concepts of Logic Programming and Machine Learning, its declarative character allows musical knowledge to  ...  "inductive logic programming" AND knowledge AND representation 4.  ... 
doi:10.34115/basrv5n4-009 fatcat:hzu3wcgi4jcavedh3bamouwbom

Page 4915 of Mathematical Reviews Vol. , Issue 95h [page]

1995 Mathematical Reviews  
In Chapter 6, the author extends the framework of positive logic programs by adding strong negation, and identifies the resulting logic programming system as a fragment of Nelson’s paraconsistent constructive  ...  It is more general than that of a logic (i.e., a consequence relation). A standard logic can be viewed as a special kind of KRS.  ... 

Combining Multiple Knowledge Representation Technologies into Agent Programming Languages [chapter]

Mehdi M. Dastani, Koen V. Hindriks, Peter Novák, Nick A. M. Tinnemeier
2009 Lecture Notes in Computer Science  
In most agent programming languages in practice a programmer is committed to the use of a single knowledge representation technology. In this paper we argue this is not necessarily so.  ...  Two techniques to deal with these issues which enable the integration of multiple knowledge representation techniques are presented: a meaning-preserving translation approach that maps one representation  ...  The agent logic program consists of a set of if-then-else rules regarded as a logic program.  ... 
doi:10.1007/978-3-540-93920-7_5 fatcat:s2db5tlf5reanehrx774llg3r4

Soft Systems Models for Knowledge Elicitation and Representation

Frank H. Gregory
1995 Journal of the Operational Research Society  
The appendix deals with the technical details of knowledge representation in modal logic, codification in terms of a Prolog program and how the system can be verified by non-monotonic logic implicit in  ...  This enables them to be rendered in modal logic and used as a framework for knowledge elicitation and for the design of knowledge-based systems with learning capability.  ... 
doi:10.2307/2584531 fatcat:pkkmtt42hzhvrbdhn5zp43gzlm

Origins of Answer-Set Programming - Some Background And Two Personal Accounts [article]

Victor W. Marek, Ilkka Niemela, Miroslaw Truszczynski
2011 arXiv   pre-print
logic programming.  ...  We discuss the evolution of aspects of nonmonotonic reasoning towards the computational paradigm of answer-set programming (ASP).  ...  Acknowledgments The work of the second author was partially supported by the Academy of Finland (project 122399). The work of the third author was partially supported by the NSF grant IIS-0913459.  ... 
arXiv:1108.3281v1 fatcat:kioh3cangrchbnkk5heilbhloi

Methodologies and Technologies for Rule-Based Systems Design and Implementation. Towards Hybrid Knowledge Engineering [chapter]

Grzegorz J. Nalepa
2008 Studies in Computational Intelligence  
It is a top-down hierarchical design methodology, based on new knowledge representation methods (XTT and ARD), on-line logical system analysis in Prolog, and XMLbased knowledge encoding.  ...  A preview of the Hekate project, which aims at developing a hybrid knowledge engineering methodology is also given.  ...  XTT as a design and knowledge representation method offers transparent, high density knowledge representation as well as a formally defined logical, Prolog-based interpretation, while preserving flexibility  ... 
doi:10.1007/978-3-540-77475-4_12 fatcat:uheal5zrazb5jn3jnyy2vunkq4

Answer Set Programming: An Introduction to the Special Issue

Gerhard Brewka, Thomas Eiter, Miroslaw Truszczynski
2016 The AI Magazine  
This editorial introduces answer set programming, a vibrant research area in computational knowledge representation and declarative programming.  ...  We give a brief overview of the articles that form this special issue on answer set programming and of the main topics they discuss.  ...  those of the Association of Logic Programming and Knowledge Representation and Reasoning Inc.  ... 
doi:10.1609/aimag.v37i3.2669 fatcat:vbguf2e4xzd3zimoxusdtjmyfi

A note on the Declarative reading(s) of Logic Programming [article]

Marc Denecker
2000 arXiv   pre-print
One common view is that logic programming is a logic for default reasoning, a sub-formalism of default logic or autoepistemic logic. In this view, negation as failure is a modal operator.  ...  This paper analyses the declarative readings of logic programming. Logic programming - and negation as failure - has no unique declarative reading.  ...  Logic programming extensions were designed as a way to solve a number of serious disadvantages of logic programming for knowledge representation.  ... 
arXiv:cs/0003056v1 fatcat:r3ihirojojfcvpfsibut2n3brm

A Three-Step Approach to Teaching Logic Models

R RENGER
2002 American Journal of Evaluation  
A logic model is a visual representation of a plausible and sensible method of how a program will work under certain conditions to solve identified problems and is fundamental to program evaluation (  ...  ABSTRACT Developing a logic model is an essential first step in program evaluation.  ...  as to how to develop a logic model.  ... 
doi:10.1016/s1098-2140(02)00230-8 fatcat:bpxspvwwarg67alpagostimo4u

Declarative Learning-Based Programming as an Interface to AI Systems

Parisa Kordjamshidi, Dan Roth, Kristian Kersting
2022 Frontiers in Artificial Intelligence  
We classify the existing frameworks based on the type of techniques and their data and knowledge representations, compare the ways the current tools address the challenges of programming real-world applications  ...  Data-driven approaches are becoming increasingly common as problem-solving tools in many areas of science and technology.  ...  When compared to probabilistic programming languages, in addition to the logical reasoning aspect, they bring in capabilities of higher order and compact logical representations of the domain knowledge  ... 
doi:10.3389/frai.2022.755361 pmid:35372833 pmcid:PMC8967162 fatcat:7fc4g77knjadlkrwz3crbmd5oq
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